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Multimedia, IEEE Transactions on

Issue 4 • Date June 2013

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Displaying Results 1 - 25 of 27
  • Table of contents

    Page(s): C1 - C4
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    Freely Available from IEEE
  • IEEE Transactions on Multimedia publication information

    Page(s): C2
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    Freely Available from IEEE
  • Guest Editorial - Special section on cloud-based mobile media: Infrastructure, services, and applications

    Page(s): 721 - 722
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    Freely Available from IEEE
  • Efficient Resource Provisioning and Rate Selection for Stream Mining in a Community Cloud

    Page(s): 723 - 734
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    Real-time stream mining such as surveillance and personal health monitoring, which involves sophisticated mathematical operations, is computation-intensive and prohibitive for mobile devices due to the hardware/computation constraints. To satisfy the growing demand for stream mining in mobile networks, we propose to employ a cloud-based stream mining system in which the mobile devices send via wireless links unclassified media streams to the cloud for classification. We aim at minimizing the classification-energy cost, defined as an affine combination of classification cost and energy consumption at the cloud, subject to an average stream mining delay constraint (which is important in real-time applications). To address the challenge of time-varying wireless channel conditions without a priori information about the channel statistics, we develop an online algorithm in which the cloud operator can dynamically adjust its resource provisioning on the fly and the mobile devices can adapt their transmission rates to the instantaneous channel conditions. It is proved that, at the expense of increasing the average stream mining delay, the online algorithm achieves a classification-energy cost that can be pushed arbitrarily close to the minimum cost achieved by the optimal offline algorithm. Extensive simulations are conducted to validate the analysis. View full abstract»

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  • Toward Blind Scheduling in Mobile Media Cloud: Fairness, Simplicity, and Asymptotic Optimality

    Page(s): 735 - 746
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    Recent advances in mobile media cloud (MMC) make it possible for users to enjoy the multimedia applications at anytime and anywhere. Most existing scheduling algorithms for MMC assume that the system parameters, such as user demand rate and server service time, are known to the scheduler. However, this assumption is invalid in many practical scenarios. In this paper, we consider a blind scenario where the above system parameters are unavailable. We aim at developing a blind scheduling algorithm (BSA) that performs well across magnitudes of fairness, simplicity and asymptotic optimality for a relatively general MMC. Specifically, the blind scheduling is first formulated as a finite time horizon optimization problem and fairness is required to be maintained at each time point with a given probability from the scheduler. Next, BSA routes the new users to the media service provider (MSP) whose weighted idle time is the longest, then assigns the available MSPs according to the fairness on the idle time. Importantly, we demonstrate that BSA is asymptotically optimal in the Halfin-Whitt heavy traffic (HWHT) regime. The asymptotic optimality is in the sense that the scheduling asymptotically and stochastically minimizes the steady-state waiting time of all the users. Our analysis also shows that in the HWHT regime the heterogeneous MSP system outperforms its homogeneous MSP counterpart in terms of the user waiting time. Moreover, we apply BSA to the program recommendation system and investigate its property. View full abstract»

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  • A Network and Device Aware QoS Approach for Cloud-Based Mobile Streaming

    Page(s): 747 - 757
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    Cloud multimedia services provide an efficient, flexible, and scalable data processing method and offer a solution for the user demands of high quality and diversified multimedia. As intelligent mobile phones and wireless networks become more and more popular, network services for users are no longer limited to the home. Multimedia information can be obtained easily using mobile devices, allowing users to enjoy ubiquitous network services. Considering the limited bandwidth available for mobile streaming and different device requirements, this study presented a network and device-aware Quality of Service (QoS) approach that provides multimedia data suitable for a terminal unit environment via interactive mobile streaming services, further considering the overall network environment and adjusting the interactive transmission frequency and the dynamic multimedia transcoding, to avoid the waste of bandwidth and terminal power. Finally, this study realized a prototype of this architecture to validate the feasibility of the proposed method. According to the experiment, this method could provide efficient self-adaptive multimedia streaming services for varying bandwidth environments. View full abstract»

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  • Design QoS-Aware Multi-Path Provisioning Strategies for Efficient Cloud-Assisted SVC Video Streaming to Heterogeneous Clients

    Page(s): 758 - 768
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    We layout a network infrastructure that leverages the storage and computing power of a cloud residing in the core for collecting network status and computing multi-path scalable video coding (SVC) streaming provisioning strategies. Therefore, in addition to its conventional tasks in the application layer, the cloud also gets involved in the network layer for the optimization of routing and forwarding. We call this scheme as cloud-assisted SVC streaming, and use it to further improve the performance of SVC streaming by using close cooperation between cloud and network. Compared to source-routing based provisioning, the cloud-assisted scheme can provide more cost-effective provisioning strategies by utilizing better knowledge of network environment together with more powerful computation power. We then propose several multi-path provisioning algorithms for cloud-assisted SVC streaming in heterogeneous networks. To the best of our knowledge, these are the first proposals to work on the problem of adaptive multi-path SVC streaming under the bandwidth, delay and differential delay constraints. Our design of the provisioning algorithms starts from an approach that is based on Max Flow and an Auxiliary Graph. Several extensions are then made based on this approach to address the situations such as provisioning from multiple sources and provisioning in dynamic network environments with rapid background traffic fluctuations. Simulations in both static and dynamic network environments show that the proposed algorithms can achieve effective performance improvements in terms of request blocking probability, bandwidth utilization, packet delay, packet loss rate, and video playback quality. View full abstract»

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  • On the Investigation of Cloud-Based Mobile Media Environments With Service-Populating and QoS-Aware Mechanisms

    Page(s): 769 - 777
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    Recent advances in mobile devices and network technologies have set new trends in the way we use computers and access networks. Cloud Computing, where processing and storage resources are residing on the network is one of these trends. The other is Mobile Computing, where mobile devices such as smartphones and tablets are believed to replace personal computers by combining network connectivity, mobility, and software functionality. In the future, these devices are expected to seamlessly switch between different network providers using vertical handover mechanisms in order to maintain network connectivity at all times. This will enable mobile devices to access Cloud Services without interruption as users move around. Using current service delivery models, mobile devices moving from one geographical location to another will keep accessing those services from the local Cloud of their previous network, which might lead to moving a large volume of data over the Internet backbone over long distances. This scenario highlights the fact that user mobility will result in more congestion on the Internet. This will degrade the Quality of Service and by extension, the Quality of Experience offered by the services in the Cloud and especially multimedia services that have very tight temporal constraints in terms of bandwidth and jitter. We believe that a different approach is required to manage resources more efficiently, while improving the Quality of Service and Quality of Experience of mobile media services. This paper introduces a novel concept of Cloud-Based Mobile Media Service Delivery in which services run on localized public Clouds and are capable of populating other public Clouds in different geographical locations depending on service demands and network status. Using an analytical framework, this paper argues that as the demand for specific services increases in a location, it might be more efficient to move those services closer to that location. This will prevent th- Internet backbone from experiencing high traffic loads due to multimedia streams and will offer service providers an automated resource allocation and management mechanism for their services. View full abstract»

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  • Attribute-Based Access to Scalable Media in Cloud-Assisted Content Sharing Networks

    Page(s): 778 - 788
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    This paper presents a novel Multi-message Ciphertext Policy Attribute-Based Encryption (MCP-ABE) technique, and employs the MCP-ABE to design an access control scheme for sharing scalable media based on data consumers' attributes (e.g., age, nationality, or gender) rather than an explicit list of the consumers' names. The scheme is efficient and flexible because MCP-ABE allows a content provider to specify an access policy and encrypt multiple messages within one ciphertext such that only the users whose attributes satisfy the access policy can decrypt the ciphertext. Moreover, the paper shows how to support resource-limited mobile devices by offloading computational intensive operations to cloud servers while without compromising data privacy. View full abstract»

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  • Optimizing Cloud Resources for Delivering IPTV Services Through Virtualization

    Page(s): 789 - 801
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    Virtualized cloud-based services can take advantage of statistical multiplexing across applications to yield significant cost savings. However, achieving similar savings with real-time services can be a challenge. In this paper, we seek to lower a provider's costs for real-time IPTV services through a virtualized IPTV architecture and through intelligent time-shifting of selected services. Using Live TV and Video-on-Demand (VoD) as examples, we show that we can take advantage of the different deadlines associated with each service to effectively multiplex these services. We provide a generalized framework for computing the amount of resources needed to support multiple services, without missing the deadline for any service. We construct the problem as an optimization formulation that uses a generic cost function. We consider multiple forms for the cost function (e.g., maximum, convex and concave functions) reflecting the cost of providing the service. The solution to this formulation gives the number of servers needed at different time instants to support these services. We implement a simple mechanism for time-shifting scheduled jobs in a simulator and study the reduction in server load using real traces from an operational IPTV network. Our results show that we are able to reduce the load by ~24%(compared to a possible ~31.3% as predicted by the optimization framework). View full abstract»

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  • An Adaptive Cloud Downloading Service

    Page(s): 802 - 810
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    Video content downloading using the P2P approach is scalable, but does not always give good performance. Recently, subscription-based premium services have emerged, referred to as cloud downloading. In this service, the cloud storage and server caches user-interested content and updates the cache based on user downloading requests. If a requested video is not in the cache, the request is held in a waiting state until the cache is updated. We call this design server mode. An alternative design is to let the cloud server serve all downloading requests as soon as they arrive, behaving as a helper peer. We call this design helper mode. Our model and analysis show that both these designs are useful for certain operating regimes. The helper mode is good at handling a high request rate, while the server mode is good at scaling with video population size. We design an adaptive algorithm (AMS) to select the service mode automatically. Intuitively, AMS switches service mode from server mode to helper mode when too many peers request blocked movies, and vice versa. The ability of AMS to achieve good performance in different operating regimes is validated by simulation . View full abstract»

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  • AMES-Cloud: A Framework of Adaptive Mobile Video Streaming and Efficient Social Video Sharing in the Clouds

    Page(s): 811 - 820
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    While demands on video traffic over mobile networks have been souring, the wireless link capacity cannot keep up with the traffic demand. The gap between the traffic demand and the link capacity, along with time-varying link conditions, results in poor service quality of video streaming over mobile networks such as long buffering time and intermittent disruptions. Leveraging the cloud computing technology, we propose a new mobile video streaming framework, dubbed AMES-Cloud, which has two main parts: adaptive mobile video streaming (AMoV) and efficient social video sharing (ESoV). AMoV and ESoV construct a private agent to provide video streaming services efficiently for each mobile user. For a given user, AMoV lets her private agent adaptively adjust her streaming flow with a scalable video coding technique based on the feedback of link quality. Likewise, ESoV monitors the social network interactions among mobile users, and their private agents try to prefetch video content in advance. We implement a prototype of the AMES-Cloud framework to demonstrate its performance. It is shown that the private agents in the clouds can effectively provide the adaptive streaming, and perform video sharing (i.e., prefetching) based on the social network analysis. View full abstract»

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  • CloudMoV: Cloud-Based Mobile Social TV

    Page(s): 821 - 832
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    The rapidly increasing power of personal mobile devices (smartphones, tablets, etc.) is providing much richer contents and social interactions to users on the move. This trend however is throttled by the limited battery lifetime of mobile devices and unstable wireless connectivity, making the highest possible quality of service experienced by mobile users not feasible. The recent cloud computing technology, with its rich resources to compensate for the limitations of mobile devices and connections, can potentially provide an ideal platform to support the desired mobile services. Tough challenges arise on how to effectively exploit cloud resources to facilitate mobile services, especially those with stringent interaction delay requirements. In this paper, we propose the design of a Cloud-based, novel Mobile sOcial tV system (CloudMoV). The system effectively utilizes both PaaS (Platform-as-a-Service) and IaaS (Infrastructure-as-a-Service) cloud services to offer the living-room experience of video watching to a group of disparate mobile users who can interact socially while sharing the video. To guarantee good streaming quality as experienced by the mobile users with time-varying wireless connectivity, we employ a surrogate for each user in the IaaS cloud for video downloading and social exchanges on behalf of the user. The surrogate performs efficient stream transcoding that matches the current connectivity quality of the mobile user. Given the battery life as a key performance bottleneck, we advocate the use of burst transmission from the surrogates to the mobile users, and carefully decide the burst size which can lead to high energy efficiency and streaming quality. Social interactions among the users, in terms of spontaneous textual exchanges, are effectively achieved by efficient designs of data storage with BigTable and dynamic handling of large volumes of concurrent messages in a typical PaaS cloud. These various designs for flexible transcoding capabilities- battery efficiency of mobile devices and spontaneous social interactivity together provide an ideal platform for mobile social TV services. We have implemented CloudMoV on Amazon EC2 and Google App Engine and verified its superior performance based on real-world experiments. View full abstract»

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  • Hessian Regularized Support Vector Machines for Mobile Image Annotation on the Cloud

    Page(s): 833 - 844
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    With the rapid development of the cloud computing and mobile service, users expect a better experience through multimedia computing, such as automatic or semi-automatic personal image and video organization and intelligent user interface. These functions heavily depend on the success of image understanding, and thus large-scale image annotation has received intensive attention in recent years. The collaboration between mobile and cloud opens a new avenue for image annotation, because the heavy computation can be transferred to the cloud for immediately responding user actions. In this paper, we present a scheme for image annotation on the cloud, which transmits mobile images compressed by Hamming compressed sensing to the cloud and conducts semantic annotation through a novel Hessian regularized support vector machine on the cloud. We carefully explained the rationality of Hessian regularization for encoding the local geometry of the compact support of the marginal distribution and proved that Hessian regularized support vector machine in the reproducing kernel Hilbert space is equivalent to conduct Hessian regularized support vector machine in the space spanned by the principal components of the kernel principal component analysis. We conducted experiments on the PASCAL VOC'07 dataset and demonstrated the effectiveness of Hessian regularized support vector machine for large-scale image annotation. View full abstract»

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  • Cloud-Based Image Coding for Mobile Devices—Toward Thousands to One Compression

    Page(s): 845 - 857
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    Current image coding schemes make it hard to utilize external images for compression even if highly correlated images can be found in the cloud. To solve this problem, we propose a method of cloud-based image coding that is different from current image coding even on the ground. It no longer compresses images pixel by pixel and instead tries to describe images and reconstruct them from a large-scale image database via the descriptions. First, we describe an input image based on its down-sampled version and local feature descriptors. The descriptors are used to retrieve highly correlated images in the cloud and identify corresponding patches. The down-sampled image serves as a target to stitch retrieved image patches together. Second, the down-sampled image is compressed using current image coding. The feature vectors of local descriptors are predicted by the corresponding vectors extracted in the decoded down-sampled image. The predicted residual vectors are compressed by transform, quantization, and entropy coding. The experimental results show that the visual quality of reconstructed images is significantly better than that of intra-frame coding in HEVC and JPEG at thousands to one compression . View full abstract»

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  • GPS/HPS-and Wi-Fi Fingerprint-Based Location Recognition for Check-In Applications Over Smartphones in Cloud-Based LBSs

    Page(s): 858 - 869
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    This paper proposes a new location recognition algorithm for automatic check-in applications (LRACI), suited to be implemented within Smartphones, integrated in the Cloud platform and representing a service for Cloud end users. The algorithm, the performance of which is independent of the employed device, uses both global and hybrid positioning systems (GPS/HPS) and, in an opportunistic way, the presence of Wi-Fi access points (APs), through a new definition of Wi-Fi FingerPrint (FP), which is proposed in this paper. This FP definition considers the order relation among the received signal strength (RSS) rather than the absolute values. This is one of the main contributions of this paper. LRACI is designed to be employed where traditional approaches, usually based only on GPS/HPS, fail, and is aimed at finding user location, with a room-level resolution, in order to estimate the overall time spent in the location, called Permanence, instead of the simple presence. LRACI allows automatic check-in in a given location only if the users' Permanence is larger than a minimum amount of time, called Stay Length (SL), and may be exploited in the Cloud. For example, if many people check-in in a particular location (e.g., a supermarket or a post office), it means that the location is crowded. Using LRACI-based data, collected by smartphones in the Cloud and made available in the Cloud itself, end users can manage their daily activities (e.g., buying food or paying a bill) in a more efficient way. The proposal, practically implemented over Android operating system-based Smartphones, has been extensively tested. Experimental results have shown a location recognition accuracy of about 90%, opening the door to real LRACI employments. In this sense, a preliminary study of its application in the Cloud, obtained through simulation, has been provided to highlight the advantages of the LRACI features. View full abstract»

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  • Adaptive Mobile Cloud Computing to Enable Rich Mobile Multimedia Applications

    Page(s): 870 - 883
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    With worldwide shipments of smartphones (487.7 million) exceeding PCs (414.6 million including tablets) in 2011, and in the US alone, more users predicted to access the Internet from mobile devices than from PCs by 2015, clearly there is a desire to be able to use mobile devices and networks like we use PCs and wireline networks today. However, in spite of advances in the capabilities of mobile devices, a gap will continue to exist, and may even widen, with the requirements of rich multimedia applications. Mobile cloud computing can help bridge this gap, providing mobile applications the capabilities of cloud servers and storage together with the benefits of mobile devices and mobile connectivity, possibly enabling a new generation of truly ubiquitous multimedia applications on mobile devices: Cloud Mobile Media (CMM) applications. View full abstract»

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  • Learning a Contextual Multi-Thread Model for Movie/TV Scene Segmentation

    Page(s): 884 - 897
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    Compared with general videos, movies and TV shows attract a significantly larger portion of people across time and contain very rich and interesting narrative patterns of shots and scenes. In this paper, we aim to recover the inherent structure of scenes and shots in such video narratives. The obtained structure could be useful for subsequent video analysis tasks such as tracking objects across cuts, action retrieval, as well as enriching user browsing and video editing interfaces. Recent research on this problem has mainly focused on combining multiple cues such as scripts, subtitles, sound, or human faces. However, considering that visual information is sufficient for human to identify scene boundaries and some cues are not always available, we are motivated to design a purely visual approach. Observing that dialog patterns occur frequently in a movie/TV show to form a scene, we propose a probabilistic framework to imitate the authoring process. The multi-thread shot model and contextual visual dynamics are embedded into a unified framework to capture the video hierarchy. We devise an efficient algorithm to jointly learn the parameters of the unified model. Experiments on two large datasets containing six movies and 24 episodes of Lost, a popular TV show with complex plot structures, are conducted. Comparative results show that, leveraging only visual cues, our method could successfully recover complicated shot threads and outperform several approaches. Moreover, our method is fast and advantageous for large-scale computation. View full abstract»

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  • Active Bucket Categorization for High Recall Video Retrieval

    Page(s): 898 - 907
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    There are large amounts of digital video available. High recall retrieval of these requires going beyond the ranked results, which is the common target in high precision retrieval. To aid high recall retrieval, we propose Active Bucket Categorization, which is a multicategory interactive learning strategy which extends MediaTable , our multimedia categorization tool. MediaTable allows users to place video shots into buckets: user-assigned subsets of the collection. Our Active Bucket Categorization approach augments this by unobtrusively expanding these buckets with related footage from the whole collection. In this paper, we propose an architecture for active bucket-based video retrieval, evaluate two different learning strategies, and show its use in video retrieval with an evaluation using three groups of nonexpert users. One baseline group uses only the categorization features of MediaTable such as sorting and filtering on concepts and fast grid preview, but no online learning mechanisms. One group uses on-demand passive buckets. The last group uses fully automatic active buckets which autonomously add content to buckets. Results indicate a significant increase in the number of relevant items found for the two groups of users using bucket expansions, yielding the best results with fully automatic bucket expansions, thereby aiding high recall video retrieval significantly. View full abstract»

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  • Interactive Schematic Summaries for Faceted Exploration of Surveillance Video

    Page(s): 908 - 920
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    We present a scalable technique to explore surveillance videos by scatter/gather browsing of trajectories of moving objects. Trajectories are clustered according to a variety of properties, such as location, orientation, and velocity that can be selected by the users. These properties allow for faceted video exploration and refinement of previous browsing steps. The proposed approach facilitates interactive clustering of trajectories by an effective way of cluster visualization that we term schematic summaries. This novel visualization illustrates cluster summaries in a schematic, nonphotorealistic style. To reduce visual clutter, we introduce the trajectory bundling technique. Further, schematic summaries include a timeline view and a showcase view to represent the facets present in a cluster. The fusion of schematic summaries, a variety of facets, and user interaction lead to efficient hierarchical exploration of video data. Examples of different browsing scenarios and initial user feedback demonstrate the potentials of our method. View full abstract»

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  • Generating Visual Summaries of Geographic Areas Using Community-Contributed Images

    Page(s): 921 - 932
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    In this paper, we present a novel approach for automatic visual summarization of a geographic area that exploits user-contributed images and related explicit and implicit metadata collected from popular content-sharing websites. By means of this approach, we search for a limited number of representative but diverse images to represent the area within a certain radius around a specific location. Our approach is based on the random walk with restarts over a graph that models relations between images, visual features extracted from them, associated text, as well as the information on the uploader and commentators. In addition to introducing a novel edge weighting mechanism, we propose in this paper a simple but effective scheme for selecting the most representative and diverse set of images based on the information derived from the graph. We also present a novel evaluation protocol, which does not require input of human annotators, but only exploits the geographical coordinates accompanying the images in order to reflect conditions on image sets that must necessarily be fulfilled in order for users to find them representative and diverse. Experiments performed on a collection of Flickr images, captured around 207 locations in Paris, demonstrate the effectiveness of our approach. View full abstract»

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  • Bootstrapping Visual Categorization With Relevant Negatives

    Page(s): 933 - 945
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    Learning classifiers for many visual concepts are important for image categorization and retrieval. As a classifier tends to misclassify negative examples which are visually similar to positive ones, inclusion of such misclassified and thus relevant negatives should be stressed during learning. User-tagged images are abundant online, but which images are the relevant negatives remains unclear. Sampling negatives at random is the de facto standard in the literature. In this paper, we go beyond random sampling by proposing Negative Bootstrap. Given a visual concept and a few positive examples, the new algorithm iteratively finds relevant negatives. Per iteration, we learn from a small proportion of many user-tagged images, yielding an ensemble of meta classifiers. For efficient classification, we introduce Model Compression such that the classification time is independent of the ensemble size. Compared with the state of the art, we obtain relative gains of 14% and 18% on two present-day benchmarks in terms of mean average precision. For concept search in one million images, model compression reduces the search time from over 20 h to approximately 6 min. The effectiveness and efficiency, without the need of manually labeling any negatives, make negative bootstrap appealing for learning better visual concept classifiers. View full abstract»

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  • Fairness Resource Allocation in Blind Wireless Multimedia Communications

    Page(s): 946 - 956
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    Traditional α -fairness resource allocation in wireless multimedia communications assumes that the quality of experience (QoE) model (or utility function) of each user is available to the base station (BS), which may not be valid in many practical cases. In this paper, we consider a blind scenario where the BS has no knowledge of the underlying QoE model. Generally, this consideration raises two fundamental questions. Is it possible to set the fairness parameter α in a precisely mathematical specific α -fairness resource allocation schememanner? If so, is it possible to implement a specific α -fairness resource allocation scheme online? In this work, we will give positive answers to both questions. First, we characterize the tradeoff between the performance and fairness by providing an upper bound of the performance loss resulting from employing α -fairness scheme. Then, we decompose the α-fairness problem into two subproblems that describe the behaviors of the users and BS and design a bidding game for the reconciliation between the two subproblems. We demonstrate that, although all users behave selfishly, the equilibrium point of the game can realize the α-fairness efficiently, and the convergence time is reasonably short. Furthermore, we present numerical simulation results that confirm the validity of the analytical results. View full abstract»

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  • Sequential Error Concealment for Video/Images by Sparse Linear Prediction

    Page(s): 957 - 969
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    In this paper, we propose a novel sequential error concealment algorithm for video and images based on sparse linear prediction. Block-based coding schemes in packet loss environments are considered. Images are modelled by means of linear prediction, and missing macroblocks are sequentially reconstructed using the available groups of pixels. The optimal predictor coefficients are computed by applying a missing data regression imputation procedure with a sparsity constraint. Moreover, an efficient procedure for the computation of these coefficients based on an exponential approximation is also proposed. Both techniques provide high-quality reconstructions and outperform the state-of-the-art algorithms both in terms of PSNR and MS-SSIM. View full abstract»

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  • TMM EDICS

    Page(s): 970
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    Freely Available from IEEE

Aims & Scope

The scope of the Periodical is the various aspects of research in multimedia technology and applications of multimedia.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Chang Wen Chen
State University of New York at Buffalo