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MultiMedia, IEEE

Issue 4 • Date Oct.-Dec. 2014

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Displaying Results 1 - 18 of 18
  • [Front cover]

    Page(s): c1
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  • Table of contents

    Page(s): c2 - 1
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  • Deep Neural Networks: Another Tool for Multimedia Computing

    Page(s): 2 - 3
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  • Critical Multimedia

    Page(s): 4 - 7
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    In this article, I explore the concept of criticality as applied and practiced within art and design, with a view to the potential of this approach within engineering and computer science. A critical viewpoint here entails deeply reflecting on and examining the norms, values, and structures of all or some subsection of society with a view to affecting change. While critical theory has typically been the purview of philosophers, literary theorists, and sociologists, the act of criticality itself has very much been a part of artistic practice from Jonathan Swift to Shakespeare, Jenny Holzer to The Guerilla Girls. Within a contemporary context, the vitality of critical approaches across sociotechnical domains points to the value of this reflexive process in provoking necessary discussion and exchange. View full abstract»

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  • A User-Centric Media Retrieval Competition: The Video Browser Showdown 2012-2014

    Page(s): 8 - 13
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  • Forging a Close Relationship with Multimedia Communities

    Page(s): 14 - 15
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  • Local Stereo Matching with Improved Matching Cost and Disparity Refinement

    Page(s): 16 - 27
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    Recent local stereo matching methods have achieved comparable performance with global methods. However, the final disparity map still contains significant outliers. In this article, the authors propose a local stereo matching method that employs a new combined cost approach and a secondary disparity refinement mechanism. They formulate combined cost using a modified color census transform and truncated absolute differences of color and gradients. They also use symmetric guided filter for cost aggregation. Unlike in traditional stereo matching, they propose a novel secondary disparity refinement to further remove the remaining outliers. Experimental results on the Middlebury benchmark show that their method ranks fifth out of 153 submitted methods, and it's the best cost-volume filtering-based local method. Experiments on real-world sequences and depth-based applications also validate the proposed method's effectiveness. View full abstract»

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  • Real-Time Gaze Estimation with Online Calibration

    Page(s): 28 - 37
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    Gaze-tracking technology is highly valuable in many interactive and diagnostic applications. For many gaze estimation systems, calibration is an unavoidable procedure necessary to determine certain person-specific parameters, either explicitly or implicitly. Recently, several offline implicit calibration methods have been proposed to ease the calibration burden. However, the calibration procedure is still cumbersome, and gaze estimation accuracy needs further improvement. In this article, the authors present a novel 3D gaze estimation system with online calibration. The proposed system is based on a new 3D model-based gaze estimation method using a single consumer depth camera sensor (via Kinect). Unlike previous gaze estimation methods using explicit offline calibration with fixed number of calibration points or implicit calibration, their approach constantly improves person-specific eye parameters through online calibration, which enables the system to adapt gradually to a new user. The experimental results and the human-computer interaction (HCI) application show that the proposed system can work in real time with superior gaze estimation accuracy and minimal calibration burden. View full abstract»

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  • Multimodal Feature Fusion for 3D Shape Recognition and Retrieval

    Page(s): 38 - 46
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    Three-dimensional shapes contain different kinds of information that jointly characterize the shape. Traditional methods, however, perform recognition or retrieval using only one type. This article presents a 3D feature learning framework that combines different modality data effectively to promote the discriminability of unimodal features. Two independent deep belief networks (DBNs) are employed to learn high-level features from low-level features, and a restricted Boltzmann machine (RBM) is trained for mining the deep correlations between the different modalities. Experiments demonstrate that the proposed method can achieve better performance. View full abstract»

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  • Latent Subspace Projection Pursuit with Online Optimization for Robust Visual Tracking

    Page(s): 47 - 55
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    This article develops a novel subspace learning algorithm for visual tracking. Specifically, the authors first present a linear projection view to formulate subspace learning and then develop a novel framework, called Latent Subspace Projection Pursuit (LSPP), to estimate the intrinsic dimension, removing corruptions and recovering the subspace structure for observed datasets. The authors evaluate the performance of their proposed method on various synthetic and real-world datasets, and the experimental results demonstrate that LSPP can achieve significant improvements in terms of performance and reduced computational complexity for visual tracking. View full abstract»

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  • Online Learning a High-Quality Dictionary and Classifier Jointly for Multitask Object Tracking

    Page(s): 56 - 66
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    This article formulates object tracking in a particle filter framework as a binary classification problem. The method effectively exploits a priori information from training data to learn online a compact and discriminative dictionary. The method incorporates the class label information into the dictionary learning process as the classification error term and idea coding regularization term, respectively. Combined with the traditional reconstruction error, a total objective function for dictionary learning is constructed. By minimizing the total object function, the approach jointly obtains a high-quality dictionary and optimal linear classifier. Combined with multitask sparse coding, the best candidate is selected by jointly evaluating the reconstructive error and classification error. As the tracking continues, the proposed algorithm alternates between multitask sparse coding and dictionary updating. Experimental evaluations on challenging video sequences show that the proposed algorithm performs favorably against state-of-the-art methods in terms of effectiveness, accuracy, and robustness. View full abstract»

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  • Training Quality-Aware Filters for No-Reference Image Quality Assessment

    Page(s): 67 - 75
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    With the rapid increase of digital imaging and communication technology usage, there's now great demand for fast and practical image quality assessment (IQA) algorithms that can predict an image's quality as consistently as humans. The authors propose a general-purpose, no-reference image quality assessment (NR-IQA) with the goal of developing a model that does not require prior knowledge about nondistorted reference images and the types of distortions. The key is to obtain effective image representations using learning quality-aware filters (QAFs). Unlike other regression models, they also use a random forest to train the mapping from the feature space. Extensive experiments conducted on the LIVE and CSIQ datasets demonstrate that the proposed NR-IQA metric QAF can achieve better prediction performance than the other state-of-the-art approaches in terms of both prediction accuracy and generalization capability. View full abstract»

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  • Graph-Based Residence Location Inference for Social Media Users

    Page(s): 76 - 83
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    Location information in social media is becoming increasingly vital in applications such as election prediction, epidemic forecasting, and emergency detection. However, only a tiny proportion of users proactively share their residence locations (which can be used to approximate the locations of most user-generated content) in their profiles, and inferring the residence location of the remaining users is nontrivial. In this article, the authors propose a framework for residence location inference in social media by jointly considering social, visual, and textual information. They first propose a data-driven approach to explore the use of friendship locality, social proximity, and content proximity for geographically nearby users. Based on these observations, they then propose a location propagation algorithm to effectively infer residence location for social media users. They extensively evaluate the proposed method using a large-scale real dataset and achieve a 15 percent relative improvement over state-of-the-art approaches. View full abstract»

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  • Context-Adaptive Modeling for Wavelet-Domain Distributed Video Coding

    Page(s): 84 - 93
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    The authors propose a bit-level context-adaptive correlation model to exploit high-order statistical correlation for wavelet-domain distributed video coding (DVC). The magnitude and sign of each coefficient are coded separately in a bit-plane fashion. The context for magnitude bit plane are designed based on the side information (SI), the local neighborhood, and the parent coefficient. The sign bit plane takes the sign of the SI as the context. The authors also introduce SI binning to classify the SI based on its quality. The SI's class is then included in the contexts for both magnitude coding and sign coding. Experimental results show that the proposed scheme provides significant coding gain over existing DVC systems. View full abstract»

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  • Standardization of Biometric Template Protection

    Page(s): 94 - 99
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    Whether it is providing fingerprints at airport immigration desks, tagging friends on social networking sites, or logging into a smartphone, biometrics provide a fast, convenient, and unobtrusive means for access control or identity verification. Biometric template protection is an umbrella term for a class of techniques used to mitigate the security and privacy threats inherent in biometric recognition. During the past decade and a half, template protection has gained traction in academia and industry, becoming the subject of publications, patents and conferences. This article reviews the progress of standardization of biometric template protection schemes. View full abstract»

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  • Objective Self

    Page(s): 100 - 110
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  • ACM International Conference on Interactive Experiences for Television and Online Video (ACM TVX 2014)

    Page(s): 112 - c3
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  • Rock Stars of 3D Printing House Advertisement

    Page(s): c4
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Aims & Scope

The magazine contains technical information covering a broad range of issues in multimedia systems and applications

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Meet Our Editors

Editor-in-Chief
John R. Smith
IBM T.J. Watson Research Center