ACM Transactions on

Multimedia Computing, Communications, and Applications (TOMM)

Latest Articles

Creating Segments and Effects on Comics by Clustering Gaze Data

Traditional comics are increasingly being augmented with digital effects, such as recoloring, stereoscopy, and animation. An open question in this... (more)

Query Expansion for Content-Based Similarity Search Using Local and Global Features

This article presents an efficient and totally unsupervised content-based similarity search method for multimedia data objects represented by... (more)

From Annotation to Computer-Aided Diagnosis

Holistic medical multimedia systems covering end-to-end functionality from data collection to aided diagnosis are highly needed, but rare. In many hospitals, the potential value of multimedia data collected through routine examinations is not recognized. Moreover, the availability of the data is... (more)


[June 2017]

The Impact Factor for the year 2016 is now available. ACM TOMM increased its IF from 0.982 to 2.250 being now the second ranked journal in the area of Multimedia. Thank you to all the EB members, authors, reviewers and readers for this excellent results.

Special Issue on " Multi-modal Understanding of Social, Affective and Subjective Attributes of Data". Cfp . Submission deadline Oct. 1st 2017

Special Issue on "Deep Learning for Intelligent Multimedia Analytics". Cfp. Submission deadline Oct. 15 2017

[April 2017]

Special Issue on  "QoE Management for Multimedia Services". Cfp Submission deadline May 15, 2017 Extended to June 15, 2017

[April 2017]

Call for Nominations for TOMM Nicolas D. Georganas Best Paper Award 2017

The Editor-in-Chief of ACM TOMM invites nominations for the ACM TOMM Nicolas D. Georganas Best Paper. Deadline for nominations of papers published in ACM TOMM from January 2016 to December 2016, is June 15th, 2017. See the call for nomination  cfn

[February 2017]

Upcoming special issues:

- "Delay-Sensitive Video Computing in the Cloud". Cfp   Submission deadline  Aug. 20, 2017

- "QoE Management for Multimedia Services". Cfp Submission deadline May 15, 2017

- "Representation, Analysis and Recognition of 3D Humans" Call for papers 

[January 2017]

ACM TOMM AE guidelines have been added

[December 2016]

ACM TOMM Special Issue on "Delay-Sensitive Video Computing in the Cloud". Cfp Submission deadline Nov. 30, 2016 Extended to Dec. 30, 2016

[November 2016]

- ACM TOMM Special Issue on "Deep Learning for Mobile Multimedia". Cfp  Submission deadline Oct15, 2016 Extended to Nov. 25, 2016

- Special Section on "Multimedia Computing and Applications of Socio-Affective Behaviors in the Wild"Cfp Submission deadline Oct. 31, 2016 Extended to Nov. 25, 2016

- Special Section on "Multimedia Understanding via Multimodal Analytics". Cfp Submission deadline Oct. 31, 2016 Extended to Nov. 25, 2016


[September 2016]


The 2016 ACM Transactions on Multimedia Computing, Communications and Applications (TOMM) Nicolas D. Georganas Best Paper Award is provided to the paper “Cross-Platform Emerging Topic Detection and Elaboration from Multimedia Streams” (TOMM vol. 11, Issue 4) by Bing-Kun Bao, Changsheng Xu, Weiqing Min and Mohammod Shamim Hossain. 

Dr. Cheng-Hsin Hsu has been nominated the ACM TOMM Associate Editor of the Year for 2016! Congratulations to Cheng-Hsin!

[August 2016]

Call for Nominations for TOMM Nicolas D. Georganas Best Paper Award

The Editor-in-Chief of ACM TOMM invites nominations for the ACM TOMM Nicolas D. Georganas Best Paper. Deadline for nominations of papers published in ACM TOMM from January 2015 to December 2015, is September 10th, 2016. See the cfn

[June 2016]

Forthcoming Special Issues in 2017

We received 11 competitive proposals this year, and we had limited slots available, so it was a very tough decision. At the end, the following four SI proposals have been selected and scheduled as follows:

- "Deep Learning for Mobile Multimedia". Cfp  Submission deadline Oct. 15, 2016 Extended to Oct. 31, 2016

- "Representation, Analysis and Recognition of 3D Human". Cfp   Submission deadline Jan. 15, 2017 Extended to Feb. 15, 2017 

Two Special Section have been also accepted and scheduled for publication in 2017:

- "Multimedia Computing and Applications of Socio-Affective Behaviors in the Wild". Cfp Submission deadline Oct. 31, 2016

- "Multimedia Understanding via Multimodal Analytics". Cfp Submission deadline Oct. 31, 2016

[June 2016]

Forthcoming Special Issues in 2016

"Trust Management for Multimedia Big Data" - Publication date August 2016

"Multimedia Big Data: Networking" - Publication date November 2016

[February 2016]

Advisory Board

We have created the ACM TOMM Advisory Board to support the Editor in Chief in the definition and implementation of strategies with no editorial duties. The following colleagues have been appointed as members of the ACM TOMM Advisory Board: Prof. Wen Gao,  Peking University, Prof. Arnold Smeulders, University of Amsterdam, Prof. Nicu Sebe, University of Trento. 

[January 2016]

New Assistant Information Director

Starting on January 1st 2016, Marco Bertini will be in charge of Assistant Information Director of ACM TOMM. 

[January 2016]

New Information Director         Starting on January 1st 2016, Stefano Berretti will be in charge of Information Director of ACM TOMM.  

[January 2016]

New Editor-in-Chief

After the end of the second term of Ralf Steinmetz, Alberto Del Bimbo from the University of Florence will be the next TOMM Editor-in-Chief starting on January 1st 2016. 

ACM TOMM Nicolas D. Georgans Best Paper Award 2015

The award goes to the article "A Quality of Experience Model for Haptic Virtual Environments” (TOMM vol.10, Issue 3) by Abdelwahab Hamam, Abdulmotaleb El Saddik and Jihad Alja'am. Congratulations!

ACM TOMM Associate Editor of the Year 2015 Award

The award goes Pradeep Atrey from State University of New York, USA for his excellent work for the journal. Congratulations!

CfP: Special Issue "Multimedia Big Data: Networking"

Please consider submitting to the second special issue in next years special issue series. Call for Papers


CfP: Special Issue "Trust Management for Multimedia Big Data"

Next year, TOMM will feature a special issue series on "Multimedia Big Data". First topic will be "Trust Management". Extended Deadline: October 15th! Call for Papers


Call for Nominations TOMM Editor-in-Chief

After two terms of the current EiC Ralf Steinmetz, the search committee started the search for a new Editor-in-Chief. Call for Nominations


New ACM submission templates

The new ACM submission templates are online. Please use the most recent link on the authors' guide to find the files.


About TOMM


A peer-reviewed, quarterly archival journal in print and digital form, TOMM consists primarily of research papers of lasting importance and value in the field of multimedia computing, communications and applications. 

News archive
Forthcoming Articles
Modeling and Analysis of Power Consumption in Live Video Streaming Systems

This paper develops an aggregate power consumption model for many-to-one live video streaming systems, such as video surveillance, where multiple video sources stream videos to a central monitoring station. In such systems, power consumption is a major concern, especially for battery-powered video sources. We model the video capturing, encoding, and transmission aspects and then provide an overall model of the power consumed by the video cameras and/or sensors. The developed model captures the following main parameters: resolution, frame rate, quantization, motion estimation range, and number of reference frames. We also analyze the power consumed by the monitoring station, which is due to video reception, potential video upscaling, and video decoding of all received video streams. In addition to modeling the power consumption, we model the achieved bitrate of video encoding. We validate and analyze the power consumption models of each phase as well the aggregate power consumption model through extensive experiments. The analysis includes examining individual parameters separately and examining the impacts of changing more than one parameter at a time.

Deep Learning for Mobile Multimedia: A Survey

Deep Learning has become a crucial technology for multimedia computing. It offers a powerful instrument to automatically produce high-level abstractions of complex multimedia data, which can be exploited in a number of applications including object detection and recognition, speech-to- text, media retrieval, multimodal data analysis, and so on. Œe availability of a‚ordable large-scale parallel processing architectures, and the sharing of e‚ective open-source codes implementing the basic learning algorithms, caused a rapid di‚usion of deep learning methodologies, bringing a number of new technologies and applications that outperform in most cases traditional machine learning technologies. In recent years, the possibility of implementing deep learning technologies on mobile devices has aŠracted signi€cant aŠention. Œanks to this technology, portable devices may become smart objects able to learn and act. Œe path towards these exciting future scenarios, however, entangles a number of important research challenges. Deep learning architectures and algorithms are hardly adapted to the storage and computation resources of a mobile device. Œerefore, there is a need for new generations of mobile processors and chipsets, small footprint learning and inference algorithms, new models of collaborative and distributed processing, and a number of other fundamental building blocks. Œis survey reports the state of the art in this exciting research area, looking back to the evolution of neural networks, and arriving to the most recent results in terms of methodologies, technologies and applications for mobile environments.

Mixtape: Using Real-time User Feedback to Navigate Large Media Collections

In this work we explore the increasing demand for novel user interfaces to navigate large media collections. We implement a geometric data structure to store and retrieve item-to-item similarity information and propose a novel navigation framework that uses vector operations and real-time user feedback to direct the outcome. The framework is scalable to large media collections and is suitable for computationally-constrained devices. In particular, we implement this framework in the domain of music. To evaluate the effectiveness of the navigation process, we propose an automatic evaluation framework, based on synthetic user profiles, which allows to quickly simulate and compare navigation paths using different algorithms and datasets. Moreover, we perform a real user study. To do that, we developed and launched Mixtape, a simple web application that allows users to create playlists by providing real-time feedback through liking and skipping patterns.

O-Mopsi: Mobile Orienteering Game for Sightseeing, Exercising and Education

Location-based games have been around already since 2000 but only recently when PokemonGo came to markets it become clear that they can reach wide popularity. In this paper, we make literature-based analytical study of what kind of issues location-based game design faces, and how they can be solved. We study how to use and verify the location, the role of the games as exergames, use in education, and study technical and safety issues. As a case study, we present O Mopsi game that combines physical activity with problem solving. It includes three challenges: (1) navigating to the next target, (2) deciding the order of targets, (3) physical movement. All of them are unavoidable and relevant. For guiding the players, we use three types of multimedia: images (targets and maps), sound (user guidance), and GPS (for positioning). We discuss about motivational aspects, analysis of the playing, and content creation. Quality of experiences are reported based on playing in SciFest Science festivals during 2011-2016.

Caching Online Video: Analysis and Proposed Algorithm

Online video presents new challenges to traditional caching with over a thousand fold increase in number of assets, rapidly changing popularity of assets and much higher throughput requirements. We propose a new hierarchical filtering algorithm for caching online videoHiFi. Our algorithm is designed to optimize hit-rate, replacement rate and cache throughput. It has an associated implementation complexity comparable to that of LRU. Our results show that under typical operator conditions, HiFi can increase edge cache byte hit-rate by 5-24% over an LRU policy, but more importantly can increase RAM or memory byte hit-rate by 80% to 200% and reduce replacement rate by more than 100 times! These two factors combined can dramatically increase throughput for most caches. If SSDs are used for storage, the much lower replacement rate may also allow substitution of lower cost MLC based SSDs instead of SLC based SSDs. We extend previous multi-tier analytical models for LRU caches to caches with filtering. We analytically show how HiFi approaches the performance of an optimal caching policy and how to tune HiFi to reach as close to optimal performance as the traffic conditions allow. We develop a realistic simulation environment for online video using statistics from operator traces. We show that HiFi performs within a few percentage points from the optimal solution which was simulated by Beladys MIN algorithm under typical operator conditions.

An Efficient Computation Framework for Connection Discovery using Shared Images

With the advent and popularity of the social network, social graphs become essential to improve services and information relevance to users for many social media applications to predict follower/followee relationship, community membership, etc. However, the social graphs could be hidden by users due to privacy concerns, or kept by social media. Recently, connections discovered from user shared images using non-user generated labels are proved to be more accessible alternatives to social graphs. But real-time discovery is difficult due to high complexity, and many applications are not possible. This paper proposes an efficient computation framework for connection discovery using user shared images. The framework applies the architecture divided into online and offline computation to facilitate faster processing for connection discovery using user shared images. Furthermore, this paper devises a general and scalable online computation framework in which many algorithms can fit into the framework to help discover connections on the fly. The performance of the framework is evaluated on the application of follower/followee recommendation with 300K+ user shared images from two social networks. It is proven that the proposed computation framework on average reduces 90% as much time as existing frameworks, with 90% as accurate as those frameworks with discovered connections.

Performance Analysis of Game Engines on Mobile and Fixed Devices

Mobile Gaming is an emerging concept, wherein gamers are using mobile devices, like smart phones and tablets, to play best seller games. Compared to dedicated gaming boxes or PCs, these devices still fall short of executing newly complex 3D-video games with a rich immersion. Three novel solutions, relying on cloud computing infrastructure, namely Computation Offloading, Cloud Gaming, and Traditional Client-Server architecture will represent the next generation of game engine architecture aiming at improving the gaming experiences and immersions. The basis of these above-mentioned solutions is the distribution of the game code over different devices (including set-top-boxes, PCs, and servers). In order to know how the game code should be distributed, advanced knowledge of game engines is required. By consequence, dissecting and analyzing game engine performances will surely help to better understand how to move in these new directions (i.e., distribute game code), which is so far missing in the literature. Aiming at filling this gap, we propose in this paper to analyze and evaluate one of the famous engines in the market, i.e., Unity 3D. We begin by detailing the architecture and the game logic of game engines. Then, we use a test-bed to evaluate the CPU and GPU consumption per frame and per module for five representative games on three platforms, namely a stand-alone computer, embedded systems and web players. Based on the obtained results and observations, we build a valued graph of each module, composing the Unity 3D architecture, which reflects the internal flow and CPU consumption.

Image Captioning with Deep Bidirectional LSTMs and Multi-Task Learning

Generating a novel and descriptive caption of an image is drawing increasing interests in computer vision, natural language processing and multimedia communities. In this work, we propose an end-to-end trainable deep bidirectional LSTM (Bi-LSTM(Long-Short Term Memory)) model to address the problem. By combining a deep convolutional neural network (CNN) and two separate LSTM networks, our model is capable of learning long term visual-language interactions by making use of history and future context information at high level semantic space. We also explore deep multimodal bidirectional models, in which we increase the depth of nonlinearity transition in different way to learn hierarchical visual-language embeddings. Data augmentation techniques such as multi-crop, multi-scale and vertical mirror are proposed to prevent overfitting in training deep models. To understand how our models "translate'' image to sentence, we visualize and qualitatively analyze the evolution of Bi-LSTM internal states over time. The effectiveness and generality of proposed models are evaluated on four benchmark datasets: Flickr8K, Flickr30K, MSCOCO and Pascal1K datasets. We demonstrate that Bi-LSTM models achieve state-of-the-art results on both caption generation and image-sentence retrieval even without integrating additional mechanism (e.g. object detection, attention model etc.). Our experiments also proves that multi-task learning is beneficial to increase model generality and gain performance. We also demonstrate our transfer learning performance of Bi-LSTM model significantly outperforms previous methods on Pascal1K dataset.

When Smart Devices Interact with Pervasive Screens: A Survey

The meeting of pervasive screens and ubiquitous smart devices in smart city has brought interaction revolution to Screen-Smart device Interaction (SSI). SSI leverages the rich features of smart device to interact with the connected-screens which have dispersed into every corners of the city. Despite the promising features of SSI, most current surveys focus their studies on the direct human-screen interaction, to the best of our knowledge, none of the surveys have investigated the state-of-the-art of SSI. This survey aims to examine the SSI from various perspectives, including both hardware implementation (i.e., screen and smart device) and software development (i.e., interaction modality and the multimedia content). The survey is structured into three main elements according to the notion of SSI, i.e., screen, smart device and interaction. Two evaluation metrics which can be used to benchmark the SSI performance in term of interaction latency and accuracy are discussed. Further, the interaction scalability to support simultaneous interaction with multiple screens and smart devices are investigated. The advantageous of SSI in stimulating social interaction and reflecting a smart lifestyle in smart city are identified. Lastly, future research challenges and opportunities are highlighted for the next generation of SSI.

Introduction to Special Issue on Deep Learning for Mobile Multimedia

Interactive Film Recombination

In this paper we discuss an innovative media entertainment application called Interactive Movietelling. As an offspring of Interactive Storytelling applied to movies, we propose to integrate narrative generation through AI planning with video processing and modeling to construct filmic variants starting from the baseline content. The integration is possible thanks to content description using semantic attributes pertaining to intermediate-level concepts shared between video processing and planning levels. The output is a recombination of segments taken from the input movie performed so as to convey an alternative plot. User tests on the prototype proved how promising Interactive Movietelling might be, even if it was designed at a proof of concept level. Possible improvements that are suggested here lead to many challenging research issues.

Securing Speech Noise Reduction in Outsourced Environment

Cloud Data Centers (CDCs) are becoming the cost effective methods for processing and storage of multimedia data including image, video and audio. Since CDCs are physically located in different jurisdictions, and are managed by external parties, data security is a growing concern. Data encryption at CDCs is commonly practiced to improve data security. However, to process the data at CDCs, data must often be decrypted, which raises issues in security. Thus, there is a growing demand for data processing techniques in encrypted domain. In this paper we analyze encrypted domain speech content processing techniques for noise reduction. Noise contaminates speech during transmission or during the acquisition process by recording. As a result, the quality of the speech content is degraded. We apply Shamir's Secret Sharing (SSS) as the cryptosystem to encrypt speech data before uploading it to a CDC. We then propose finite impulse response (FIR) digital filters to reduce white and wind noise in the speech in encrypted domain. We prove that our proposed schemes meet the security requirements of efficiency, accuracy and checkability for both semi-honest and malicious adversarial models. Experimental results show that our proposed filtering techniques for speech noise reduction in Encrypted Domain (ED) produce similar results when compared to Plaintext Domain (PD) processing.

Complexity Correlation-Based CTU-Level Rate Control with Direction Selection for HEVC

Rate control is a crucial consideration in high-efficiency video coding (HEVC), but the CTU-level rate control model often fails due to inadequate consideration of the correlation of the neighboring CTU. In this study, we established a novel complexity correlation-based CTU level rate control for HEVC. First, we formulated the model parameter estimation scheme as a multivariable estimation problem; second, based on the complexity correlation of the neighboring CTU, an optimal direction is selected in five directions for CTU selection during model parameter estimation to further improve the prediction accuracy of the encoding CTU complexity. The most relative CTUs in the optimal direction are used to estimate current CTU model parameters. Third, in order to improve their precision, the estimated model parameters are solved via the estimated complexity of their relationship to the CTUs. Experimental results showed that the proposed algorithm can significantly improve the accuracy of the CTU-level rate control and thus the coding performance; the proposed scheme consistently outperformed HM16.0 and other state-of-the-art algorithms in a variety of testing configurations. More specifically, up to 8.9% and on average 6.4% BD-Rate reduction was achieved compared to HM16.0 and up to 4.2% and an average of 2.5% BD-Rate reduction was achieved compared to other algorithms, with only a slight complexity overhead.

Saliency Detection on Light Field: A Multi-Cue Approach

Saliency detection has recently received extensive research interest beyond 2D features. Despite the many available capturing devices and algorithms, there still exists a wide spectrum of challenges that need to be addressed to achieve accurate saliency detection. Inspired by the success of light field technology, in this paper, we propose a new computational scheme to detect salient regions by investigating multiple visual cues from light-field images. First, saliency prior maps are generated from several light-field features based on superpixel-level intra-cue distinctiveness, such as color, depth, and flow inherited from different focal planes and multiple viewpoints. Then, these maps are merged into a single map using a random-search-based weighting approach, and a Gaussian-based location cue is proposed to enhance the integrated saliency values. Finally, structure-preserving graph-based methods are employed to effectively refine the object details and enhance the accuracy of the saliency map. Since the widely used light-field saliency dataset LFSD [Li et al. 2014] is not sufficiently challenging, we present a new light-field saliency analysis benchmark dataset, named HFUT-Lytro, which consists of 255 light fields with a range from 53 to 64 images generated from each light-field sample, therein spanning multiple occurrences of saliency detection challenges such as occlusions, cluttered background and appearance changes. The experimental results demonstrate the effectiveness of the proposed approach and show that our approach achieves 0.6-6.7% relative improvements over state-of-the-art methods in terms of the F-measure and precision performance evaluation metrics.

Sparse Representation-Based Semi-Supervised Regression for People Counting

Label imbalance and the insufficiency of labeled training samples are major obstacles in most methods for counting people in images or videos. In this work, a sparse representation-based semi-supervised regression method is proposed to count people in images with limited data. The basic idea is to predict the unlabeled training data, select reliable samples to expand the labeled training set and retrain the regression model. In the algorithm, the initial regression model, which is learned from the labeled training data, is used to predict the number of people in the unlabeled training dataset. Then the unlabeled training samples are regarded as an over-complete dictionary. Each feature of the labeled training data can be expressed as a sparse linear approximation of the unlabeled data. In turn, the labels of the labeled training data can be estimated based on a sparse reconstruction in feature space. The label confidence in labeling an unlabeled sample is estimated by calculating the reconstruction error. The training set is updated by selecting unlabeled samples with minimal reconstruction errors, and the regression model is retrained on the new training set. A co-training style method is applied during the training process. The experimental results demonstrate that the proposed method has a low mean square error and mean absolute error compared with state-of-the-art people-counting benchmarks.

Pithos: Distributed Storage for Massive Multi-user Virtual Environments

There has been significant research effort into Peer-to-Peer (P2P) Massively Multi-user Virtual Environments (MMVEs). A number of architectures have been proposed to implement the P2P approach; however, the development of fully distributed MMVEs has met with a number of challenges. In this work we address one of the key remaining challenges of state consistency and persistency in P2P MMVEs. Having reviewed state management and persistency architectures currently receiving research attention, we have identified deficiencies such as lack of fairness, responsiveness and scalability. To address these deficiencies, we present Pithos -- a reliable, responsive, secure, fair and scalable distributed storage system, suited to P2P MMVEs. Pithos is designed specifically for P2P MMVEs and we show that it improves the reliability and responsiveness of storage architectures as compared to existing P2P state persistency architectures. Pithos is implemented as an OverSim simulation running on the OMNeT++ network simulation framework. It is evaluated using up to 10,400 peers, with realistic latency profiles, with up to 15.8 million storage and retrieval requests that are generated to store a total of 2.4 million objects. Each peer in Pithos uses a maximum of 1950 Bps bandwidth to achieve 99.98% storage reliability, while the most reliable overlay storage configuration tested only achieved 93.65% reliability, using 2182 Bps bandwidth. Pithos is also more responsive than overlay storage, with an average responsiveness of 0.192s, compared with the average overlay responsiveness of 1.4s when retrieving objects from storage.

Multimodal Retrieval with Diversification and Relevance Feedback for Tourist Attraction Images

In this paper, we present a novel framework that can produce a visual description of a tourist attraction by choosing the most diverse pictures from community-contributed datasets, that describe different details of the queried location. The main strength of the proposed approach is its flexibility that permits to filter out non-relevant images, and to obtain a reliable set of diverse and relevant images by first clustering similar images according to their textual descriptions and their visual content, and then extracting images from different clusters according to a measure of user's credibility. Clustering is based on a two-step process where textual descriptions are used first, and the clusters are then refined according to the visual features. The degree of diversification can be further increased by exploiting users' judgments on the results produced by the proposed algorithm through a novel approach, where users not only provide a relevance feedback, but also a diversity feedback. Experimental results performed on the MediaEval 2015 ``Retrieving Diverse Social Images" dataset show that the proposed framework can achieve very good performance both in the case of automatic retrieval of diverse images, and in the case of the exploitation of the users' feedback. The effectiveness of the proposed approach has been also confirmed by a small case study involving a number of real users.

Multimodal Multiplatform Social Media Event Summarization

Social media platforms are turning into important news sources for users since they provide real-time information with a wide range of perspectives. However, high volume, dynamism, noise and redundancy exhibited by social media data create difficulties for users in comprehending the entire content. Recent works emphasize on summarizing the content of either a single social media platform or of a single modality (either textual or visual). However, each platform has its own unique characteristics and user base, which brings to light different aspects of real-world events. This makes it critical as well as challenging to combine textual and visual data from different platforms. In this article, we propose summarization of real-word events with data stemming from different platforms and multiple modalities. We present the use of Markov Random Fields based similarity measure to link content across multiple platforms. This measure also enables the linking of content across time which is useful for tracking the evolution of long-running events. For the final content selection, summarization is modeled as a subset selection problem. To handle the complexity of the optimal subset selection, we propose the use of submodular objectives. Facets such as coverage, novelty and significance are modeled as submodular objectives in a multimodal social media setting. We conduct a series of quantitative and qualitative experiments to illustrate the effectiveness of our approach compared to alternative methods.

Structure-aware Multi-modal Feature Fusion for RGB-D Scene Classification and Beyond

While convolutional neural networks (CNN) have been excellent for object recognition, the greater spatial variability in scene images typically mean that the standard full-image CNN features are suboptimal for scene classification. In this paper, we investigate a framework allowing greater spatial flexibility, in which the Fisher vector (FV) encoded distribution of local CNN features, obtained from a multitude of region proposals per image, is considered instead. The CNN features are computed from an augmented pixel-wise representation comprising multiple modalities of RGB, HHA and surface normals, as extracted from RGB-D data. More significantly, we make two postulates: (1) component sparsity --- that only a small variety of region proposals and their corresponding FV GMM components contribute to scene discriminability, and (2) modal non-sparsity --- that features from all modalities are encouraged to co-exist. In our proposed feature fusion framework, these are implemented through regularization terms that apply group lasso to GMM components and exclusive group lasso across modalities. By learning and combining regressors for both proposal-based FV features and global CNN features, we are able to achieve state-of-the-art scene classification performance on the SUNRGBD Dataset and NYU Depth Dataset V2. Moreover, we further apply our feature fusion framework on action recognition task to demonstrate that our framework can be generalized for other multi-modal well-structured features. In particular, for action recognition, we enforce inter-part sparsity to choose more discriminative body parts, and inter-modal non-sparsity to make informative features from both appearance and motion modalities to co-exist. Experimental results on JHMDB and MPII Cooking datasets show that our feature fusion is also very effective for action recognition, achieving very competitive performance compared with the state-of-the-art.

Best Papers of the 2016 ACM Multimedia Systems (MMSys) conference and workshop on Network and Operating System Support for Digital Audio and Video (NOSSDAV) 2016


Publication Years 2005-2017
Publication Count 513
Citation Count 2829
Available for Download 512
Downloads (6 weeks) 3252
Downloads (12 Months) 28627
Downloads (cumulative) 239429
Average downloads per article 468
Average citations per article 6
First Name Last Name Award
El Saddik Abdulmotaleb ACM Distinguished Member (2010)
ACM Senior Member (2008)
Ruzena R Bajcsy ACM Distinguished Service Award (2003)
ACM AAAI Allen Newell Award (2001)
Susanne Boll ACM Senior Member (2012)
Surendar Chandra ACM Senior Member (2009)
Kuan-Ta Chen ACM Senior Member (2015)
Matthew L Cooper ACM Distinguished Member (2016)
ACM Senior Member (2010)
Alberto Del Bimbo ACM Distinguished Member (2016)
Claudio A. Feijoo ACM Senior Member (2009)
Shahram Ghandeharizadeh ACM Software System Award (2008)
Andreas Girgensohn ACM Distinguished Member (2008)
Michael L Gleicher ACM Distinguished Member (2011)
Tracy Anne Hammond ACM Senior Member (2015)
Lynda Hardman ACM Distinguished Member (2014)
ACM Senior Member (2013)
Xian-Sheng Hua ACM Distinguished Member (2015)
ACM Senior Member (2009)
Tiejun Huang ACM Senior Member (2013)
Tao Mei ACM Distinguished Member (2016)
ACM Senior Member (2012)
Filippo Menczer ACM Distinguished Member (2013)
Saraju P. Mohanty ACM Senior Member (2010)
Nuria Oliver ACM Distinguished Member (2015)
ACM Senior Member (2013)
Ming Ouhyoung ACM Senior Member (2007)
Sethuraman Panchanathan ACM Senior Member (2009)
Joel Jose Rodrigues ACM Senior Member (2011)
Yong Rui ACM Distinguished Member (2009)
ACM Senior Member (2006)
David Ayman Shamma ACM Distinguished Member (2016)
ACM Senior Member (2011)
Prashant J Shenoy ACM Distinguished Member (2009)
ACM Senior Member (2006)
Frank Shipman ACM Distinguished Member (2009)
Shervin Shirmohammadi ACM Senior Member (2017)
Bart Thomee ACM Senior Member (2016)
Matthew A Turk ACM Senior Member (2007)
Shuicheng Yan ACM Distinguished Member (2016)
Lei Zhang ACM Senior Member (2011)
Michelle Zhou ACM Distinguished Member (2009)
ACM Senior Member (2007)
Michelle Zhou ACM Distinguished Member (2009)
ACM Senior Member (2007)

First Name Last Name Paper Counts
Changsheng Xu 15
Tatseng Chua 14
Shuicheng Yan 14
Mohamed Hefeeda 14
Mohan Kankanhalli 10
Weitsang Ooi 10
Klara Nahrstedt 10
Chenghsin Hsu 8
Tao Mei 7
Gheorghita Ghinea 7
Roger Zimmermann 7
Pradeep Atrey 6
Ralf Steinmetz 6
Carsten Griwodz 6
Pål Halvorsen 6
Yong Rui 6
Svetha Venkatesh 5
Abdulmotaleb El Saddik 5
Mohammad Hossain 5
Abdulmotaleb El Saddik 5
Meng Wang 5
Balakrishnan Prabhakaran 5
Shihfu Chang 5
Hari Sundaram 5
Jitao Sang 5
Qi Tian 5
Changwen Chen 5
Shervin Shirmohammadi 4
Namunu Maddage 4
Dick Bulterman 4
Xiansheng HUA 4
Zongpeng Li 4
Shipeng Li 4
Pablo César 4
Shueng Chan 4
Wolfgang Effelsberg 4
Ramesh Jain 4
Oluwakemi Ademoye 4
Gabriel Muntean 4
Prashant Shenoy 3
Marcel Worring 3
Jiangchuan Liu 3
Romulus Grigoraş 3
Géraldine Morin 3
Laurencetianruo Yang 3
Séamus McLoone 3
Eckehard Steinbach 3
Tomás Ward 3
Zhengjun Zha 3
Richang Hong 3
Alberto Del Bimbo 3
Jinhui Tang 3
Rongrong Ji 3
WeiQi Yan 3
Alan Hanjalic 3
James She 3
Ruzena Bajcsy 3
Zheng Yan 3
Jiwu Huang 3
Wenwu Zhu 3
Si Liu 3
Songqing Chen 3
Alexandru Iosup 3
Robert Deng 3
Shervin Shirmohammadi 3
Chuan Wu 3
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Quan Fang 1
Xue Li 1
Philip Chou 1
Chihhao Chiu 1
Yan Yan 1
Qiong Wu 1
Pierre Boulanger 1
Richardtianbai Ma 1
Xuening Liu 1
Bo Li 1
Bashar Qudah 1
Susanne Boll 1
Jonathan Weir 1
Hongmei Liu 1
Hui Feng 1
Amit Sachan 1
Frederick Li 1
Lewis Li 1
Arantxa Villanueva 1
Victoria Ponz 1
Yanwei Liu 1
Espen Helgedagsrud 1
Henrik Alstad 1
Mulin Chen 1
Qi Wang 1
Filip De Turck 1
Xuyong Yang 1
Stefano Petrangeli 1
Daqing Zhang 1
Markus Schedl 1
Yonghong Tian 1
Menglin Jiang 1
Mingju Wu 1
Shingchern You 1
Gerald Kunzmann 1
Xuanhui Wang 1
Dong Xu 1
Yuching Lin 1
Ou Wu 1
Subhabrata Bhattacharya 1
Fairouz Hussein 1
Yunsheng Yang 1
Jean Grégoire 1
Subhasis Chaudhuri 1
Edmond Ho 1
Nacim Ihadaddene 1
Paul Dickerson 1
Herngyow Chen 1
Surong Wang 1
Thomas De Lange 1
Sanjeev Koppal 1
Srisakul Thakolsri 1
Elaine Chew 1
Adrien Joly 1
Shojiro Nishio 1
Yang Li 1
Yu Zhang 1
Jaling Wu 1
Tyler Ballast 1
Shannon Chen 1
Juha Vierinen 1
Philippe Mulhem 1
Shihchia Huang 1
Prasant Mohapatra 1
Mika Tuomola 1
Terence Wright 1
John Oommen 1
Hengtao Shen 1
Mauricio Orozco 1
Yuli Gao 1
Asgeir Mortensen 1
Deyu Chen 1
Maximilian Ott 1
Harish Katti 1
Xiaosong Lou 1
Zhijie Shen 1
Xuanjia Qiu 1
Hai Jin 1
Ryan Spicer 1
Yanming Shen 1
K Wijayaratne 1
Polle Zellweger 1
Silvana Castano 1
Luca Ludovico 1
Anne Arigon 1
Longyu Zhang 1
Waichong Chia 1
Gareth Tyson 1
Yingqing Xu 1
Shaoyan Sun 1
Bart Thomée 1
Jing Liu 1
Peter Knees 1
Ali El Essaili 1
Jongtack Jung 1
Yunfei Chen 1
Jianguo Jiang 1
Bo Zhang 1
Francesco De Natale 1
Simone Milani 1
Patrick Flynn 1
Nicolas Alt 1
Baojie Fan 1
Lianqing Liu 1
Haibin Yu 1
Edip Demirbilek 1
Hanhui Li 1
Linxie Tang 1
Jinhui Tang 1
Dario Comanducci 1
Suiping Zhou 1
Konstantin Pogorelov 1
John Shea 1
Tiecheng Liu 1
John Kender 1
Ron Shacham 1
Suhuai Luo 1
Stefanos Antaris 1
Karine Pires 1
Xiangyang Xue 1
Xue Liu 1
Miguel Nussbaum 1
Saraju Mohanty 1
Yinpeng Chen 1
Dawoon Jung 1
William Seager 1
Yuri Ivanov 1
Mark Corner 1
Wei Wei 1
Zheng Guo 1
Frédéric Boudon 1
Yong Wei 1
Tsungnan Lin 1
Ingmar Franke 1
Yowon Jeong 1
Chonggang Wang 1
Ruyan Wang 1
Fanyu Bu 1
Jun Liu 1
Xiangbo Shu 1
Alexander Hauptmann 1
Yongdong Wu 1
Medy Sanadidi 1
Carl Hogsden 1
Elizabeth Papadopoulou 1
M Williams 1
Junho Ahn 1
Niccolò Pretto 1
Abdelwahab Hamam 1
Mika Aalto 1
Matthew Cooper 1
Karsten Schwan 1
Fadi Dornaika 1
Robert Walz 1
Joeri Van Der Velden 1
Jongseung Park 1
Irwin Sobel 1
Beitao Li 1
Gian Foresti 1
Heng Liu 1
Lei Zhang 1
Rainer Lienhart 1
Michael Driscoll 1
Kurt Keutzer 1
Yalin Lee 1
Jiajia Liu 1

Affiliation Paper Counts
Microsoft Technology Centers 1
IBM Canada Ltd. 1
IBM China Company Limited 1
CSIRO Data61 1
National Taichung University of Science and Technology 1
Baerum Hospital 1
University of Coimbra 1
University of Surrey 1
Indiana University 1
University of Canberra 1
National Taiwan Normal University 1
University of Delaware 1
North Georgia College & State University 1
National Taiwan Ocean University 1
University of Massachusetts Boston 1
National University of Defense Technology China 1
National Central University Taiwan 1
University of Missouri System 1
Sam Houston State University 1
University of Tokyo 1
Guangzhou University 1
University of Nebraska - Lincoln 1
Clemson University 1
National Chengchi University 1
Institute for Research in IT and Random Systems 1
University of Rochester 1
University of Edinburgh 1
Universidad de Granada 1
University of the Basque Country 1
State University of Londrina 1
New Mexico Institute of Mining and Technology 1
TELECOM ParisTech 1
Cancer Registry of Norway Institute of Population-Based Cancer Research 1
The University of North Carolina Wilmington 1
Dalian Maritime University 1
Kent State University 1
EURECOM Ecole d'Ingenieurs & Centre de Recherche en Systemes de Communication 1
Institut Dalle Molle D'intelligence Artificielle Perceptive 1
California State University Los Angeles 1
Saint Francis Xavier University 1
Japan National Institute of Information and Communications Technology 1
Pontifical Catholic University of Rio de Janeiro 1
Eindhoven University of Technology 1
Saarland University 1
York University Canada 1
University of Kuwait 1
Yarmouk University 1
University of Peshawar 1
HEC School of Management 1
Tata Consultancy Services India 1
General Hospital of People's Liberation Army 1
ITMO University 1
University of Qatar 1
Adobe Systems Incorporated 1
Samsung Electronics, India Software Operations Ltd. 1
Hebei Academy of Sciences 1
Hunan University of Commerce 1
British Broadcasting Corporation 1
Sybase Inc. 1
Laboratoire de Biometrie et Biologie Evolutive, Villeurbanne 1
Austrian Institute of Technology 1
Singapore University of Technology and Design 1
Laboratoire d'Automatique, Genie Informatique et Signal 1
Indian Institute of Technology Rajasthan 1
New York University Abu Dhabi 1
Guangdong University of Petrochemical Technology 1
University of Wales Trinity Saint David 1
Bowie State University 1
Sungkyul Christian University 1
Kansas State University 1
China Telecommunications 1
Thapar University 1
Advanced Telecommunications Research Institute International (ATR) 1
Guangdong Polytechnic Normal University 1
Royal Institute of Technology 1
University of Ontario Institute of Technology 1
MIT Media Laboratory 1
Polytechnic University - Brooklyn 1
Henan University 1
Uppsala University 1
Northumbria University 1
Institute of High Performance Computing, Singapore 1
Johns Hopkins University 1
Yale University 1
National Kaohsiung Marine University Taiwan 1
Huaqiao University 1
Google Inc. 1
National Changhua University of Education 1
Cornell University 1
Malmo University 1
Chung Hua University 1
Waterford Institute of Technology 1
Tohoku University 1
Hong Kong Polytechnic University 1
University of Massachusetts Dartmouth 1
University of California , Merced 1
National Research Council Canada 1
Harvard University 1
Florida Institute of Technology 1
Hohai University 1
King's College London 1
State University of New York at Albany 1
Auburn University 1
Air Force Research Laboratory Information Directorate 1
Open University 1
Incheon National University 1
University of Windsor 1
University of Zurich 1
INRIA Institut National de Rechereche en Informatique et en Automatique 1
Ca' Foscari University of Venice 1
China Agricultural University 1
Federal University of Bahia 1
Federal University of Sao Carlos 1
Zhejiang Wanli University 1
Hong Kong Baptist University 1
Eastman Kodak Company 1
University of Washington, Seattle 1
University of Pittsburgh 1
University of Kentucky 1
Carleton University 1
Hongik University 1
Nankai University 1
Vienna University of Technology 1
University of South Carolina 1
South Dakota School of Mines & Technology 1
Universite Paris Sorbonne - Paris IV 1
Deakin University 1
Karolinska University Hospital 1
Leiden University 1
Netherlands Organisation for Applied Scientific Research - TNO 1
Agder University College 1
Kodak Research Laboratories 2
Ohio State University 2
University of Modena and Reggio Emilia 2
Indian Institute of Technology, Kharagpur 2
Feng Chia University 2
University College Dublin 2
University of Electronic Science and Technology of China 2
University of Houston 2
Federal University of Rio Grande do Sul 2
The University of North Carolina at Charlotte 2
National Chung Cheng University 2
Utrecht University 2
Australian National University 2
IBM Almaden Research Center 2
University of Adelaide 2
Telecom Research Center Vienna 2
Washington University in St. Louis 2
Goldsmiths, University of London 2
Queensland University of Technology 2
RMIT University 2
Japan Advanced Institute of Science and Technology 2
Technical University of Berlin 2
University of Reading 2
University of Milan - Bicocca 2
Oldenburger Forschungs- Und Entwicklungsinstitut fur Informatik-Werkzeuge Und -Systeme 2
University of Antwerp 2
Yuan Ze University 2
National Cheng Kung University 2
University of Nottingham 2
Florida International University 2
INSA Lyon 2
Instituto de Telecomunicacoes 2
Xi'an Jiaotong University 2
Alpen-Adria-Universität Klagenfurt 2
University of Oldenburg 2
Communication University of China 2
Johannes Kepler University Linz 2
University of Durham 2
Massachusetts Institute of Technology 2
Pace University 2
National Chi Nan University 2
University of Tehran 2
Nippon Telegraph and Telephone Corporation 2
University Michigan Ann Arbor 2
Roehampton University 2
Stevens Institute of Technology 2
Sharif University of Technology 2
Lund University 2
The University of Georgia 2
University of Regina 2
Texas State University-San Marcos 2
Swinburne University of Technology 2
Tokyo Institute of Technology 2
International Computer Science Institute 2
University of Sciences and Technology Houari Boumediene 2
Institut National de la Recherche Scientifique 2
DoCoMo Communications Laboratories Europe GmbH 2
VMware, Inc 2
Indian Institute of Technology 2
Indraprastha Institute of Information Technology Delhi 2
Sunway University 2
Universidad Carlos III de Madrid 3
University of Texas at San Antonio 3
Hebrew University of Jerusalem 3
University of Sao Paulo 3
University of Salford 3
Fudan University 3
Oregon State University 3
Indian Institute of Technology, Bombay 3
University of Saskatchewan 3
Institute of Acoustics Chinese Academy of Sciences 3
IBM Thomas J. Watson Research Center 3
University of Trieste 3
Singapore Management University 3
Georgia Institute of Technology 3
Binghamton University State University of New York 3
University of Alabama in Huntsville 3
Chongqing University of Posts and Telecommunications 3
East China Normal University 3
University of Notre Dame 3
Xi'an Institute of Optics and Precision Mechanics Chinese Academy of Sciences 3
Lancaster University 3
Osaka University 3
University of North Texas 3
South China University of Technology 3
Hunan University of Science and Technology 3
University of Sydney 3
University of Udine 3
University of Vienna 3
Stony Brook University 3
Universidad Autonoma de Madrid 3
Research Organization of Information and Systems National Institute of Informatics 3
University of California, Riverside 3
Queen's University Belfast 3
University of California, San Diego 3
Shandong University 3
Umea University 3
Indian Institute of Technology, Delhi 3
Ghent University 3
Communaute d'Universites et d'Etablissements Lille Nord de France 3
Qatar Computing Research institute 3
Simula Research Laboratory 4
University of Beira Interior 4
Korea University 4
University of Illinois 4
China University of Geosciences, Wuhan 4
Brigham Young University 4
University of Montreal 4
Telefonica 4
The University of British Columbia 4
University of Newcastle, Australia 4
National Taipei University of Technology 4
King Saud University 4
Telecom Bretagne 4
Indian Institute of Science 4
University of Trento 4
Liaoning Normal University 4
Harbin Institute of Technology 4
University of Bristol 4
University of Ulster 4
National Chiayi University 4
Queen Mary, University of London 4
University of Connecticut 4
Shanghai University 4
University of California, Los Angeles 4
University of California, Santa Barbara 4
Federal University of Minas Gerais 4
University of Alberta 4
Nanjing University 4
New Jersey Institute of Technology 5
Texas A and M University 5
Shanghai Jiaotong University 5
The University of North Carolina at Chapel Hill 5
University of Exeter 5
Technical University of Madrid 5
Pontificia Universidad Catolica de Chile 5
University of Milan 5
University of Queensland 5
University of Wisconsin Madison 5
University of Padua 5
McGill University 5
University of California, Davis 5
Aristotle University of Thessaloniki 5
University of Toronto 5
University of Technology Sydney 5
Dublin City University 6
Microsoft Corporation 6
Inha University, Incheon 6
Institute of Computing Technology Chinese Academy of Sciences 6
University of Colorado at Boulder 6
Pennsylvania State University 6
Northwestern Polytechnical University China 6
University of California, Irvine 6
Purdue University 6
Wayne State University 6
Indian Institute of Technology, Guwahati 6
Worcester Polytechnic Institute 6
National Chiao Tung University Taiwan 6
Beihang University 6
Beijing University of Posts and Telecommunications 6
University of Winnipeg 6
National Tsing Hua University 6
Technical University of Dresden 6
University College London 6
Nanjing University of Post and TeleCommunications 7
Shenyang Institute of Automation Chinese Academy of Sciences 7
Athlone Institute of Technology 7
University of Florida 7
University of Turin 7
University of Florence 7
George Mason University 7
Peking University 7
Nanjing University of Science and Technology 7
Hefei University of Technology 8
Chinese University of Hong Kong 8
Microsoft Research 8
University of Amsterdam 8
Dalian University of Technology 8
University of Calgary 8
Heriot-Watt University, Edinburgh 9
University at Buffalo, State University of New York 9
University of Cambridge 9
Swiss Federal Institute of Technology, Lausanne 9
Carnegie Mellon University 9
National University of Ireland, Maynooth 9
Xidian University 9
Center for Mathematics and Computer Science - Amsterdam 9
HP Labs 9
The University of Hong Kong 9
Yahoo Research Labs 10
University of Mannheim 10
Shenzhen University 10
Universite de Toulouse 10
Korea Advanced Institute of Science & Technology 10
FX Palo Alto Laboratory 10
University of Central Florida 11
Portland State University 11
Academia Sinica Taiwan 11
University of Massachusetts Amherst 11
Aalto University 11
Brunel University London 12
Technical University of Munich 12
University of Southern California 12
Technical University of Darmstadt 13
University of Texas at Dallas 13
Delft University of Technology 13
Curtin University of Technology, Perth 13
Motorola 13
City University of Hong Kong 14
Institute of Automation Chinese Academy of Sciences 14
Hong Kong University of Science and Technology 15
Zhejiang University 15
Nanyang Technological University 16
University of California, Berkeley 17
Sun Yat-Sen University 17
National Taiwan University 18
Columbia University 19
Institute for Infocomm Research, A-Star, Singapore 19
Huazhong University of Science and Technology 21
Tsinghua University 21
University of Science and Technology of China 22
Arizona State University 24
University of Illinois at Urbana-Champaign 25
University of Oslo 27
Microsoft Research Asia 32
Simon Fraser University 32
Chinese Academy of Sciences 34
University of Ottawa, Canada 37
National University of Singapore 126

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)

Volume 13 Issue 3, May 2017  Issue-in-Progress
Volume 13 Issue 2, May 2017
Volume 13 Issue 1, January 2017

Volume 12 Issue 5s, December 2016 Special Section on Multimedia Big Data: Networking and Special Section on Best Papers From ACM MMSYS/NOSSDAV 2015
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Volume 12 Issue 4, August 2016
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Volume 12 Issue 1s, October 2015 Special Issue on Smartphone-Based Interactive Technologies, Systems, and Applications and Special Issue on Extended Best Papers from ACM Multimedia 2014
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Volume 7 Issue 4, November 2011
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