<![CDATA[ IEEE Transactions on Multimedia - new TOC ]]>
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TOC Alert for Publication# 6046 2016September29<![CDATA[Table of contents]]>1810C1C4286<![CDATA[IEEE Transactions on Multimedia]]>1810C2C254<![CDATA[Guest Editorial: Multimedia-Based Healthcare]]>181019251928192<![CDATA[Classification-Based Record Linkage With Pseudonymized Data for Epidemiological Cancer Registries]]>1810192919411619<![CDATA[ConfidentCare: A Clinical Decision Support System for Personalized Breast Cancer Screening]]>personalized screening policies tailored to the features of individuals are desirable. To address this issue, we developed ConfidentCare: a computer-aided clinical decision support system that learns a personalized screening policy from electronic health record (EHR) data. By a “personalized screening policy,” we mean a clustering of women's features, and a set of customized screening guidelines for each cluster. ConfidentCare operates by computing clusters of patients with similar features, then learning the “best” screening procedure for each cluster using a supervised learning algorithm. The algorithm ensures that the learned screening policy satisfies a predefined accuracy requirement with a high level of confidence for every cluster. By applying ConfidentCare to real-world data, we show that it outperforms the current CPGs in terms of cost efficiency and false positive rates: a reduction of 31 in the false positive rate can be achieved.]]>1810194219551785<![CDATA[Kernel Combined Sparse Representation for Disease Recognition]]>181019561968572<![CDATA[Audiovisual Spatial-Audio Analysis by Means of Sound Localization and Imaging: A Multimedia Healthcare Framework in Abdominal Sound Mapping]]>181019691976512<![CDATA[Tensor Manifold Discriminant Projections for Acceleration-Based Human Activity Recognition]]>181019771987858<![CDATA[Multiple Video Delivery in m-Health Emergency Applications]]>1810198820011955<![CDATA[Enabling Secure and Fast Indexing for Privacy-Assured Healthcare Monitoring via Compressive Sensing]]>1810200220141437<![CDATA[Depth Map Down-Sampling and Coding Based on Synthesized View Distortion]]> compared with 3D-AVC.]]>181020152022709<![CDATA[<inline-formula> <img src="/images/tex/16622.gif" alt="\lambda "> </inline-formula>-Domain Rate Control Algorithm for HEVC Scalable Extension]]> -domain rate control algorithm for high efficiency video coding (HEVC) scalable extension. All the commonly used scalabilities including temporal, spatial, and quality scalability are taken into consideration. The proposed algorithm mainly has three key contributions. First, we propose an optimal initial target bits and initial encoding parameters determination algorithm for the first frame of each layer to achieve the best rate-distortion (R-D) performance. Second, an optimal bit allocation algorithm taking both the intra and inter layer dependence into consideration is proposed for the inter frames under spatial and quality scalability cases. Third, since the coding scheme of HEVC scalable extension with multiple layers is even more flexible than HEVC, an adaptive updating algorithm for R- model is proposed to control the bits per frame even more precisely. The experimental results demonstrate that the proposed -domain rate control algorithm can bring both more precise bitrate accuracy and better R-D performance compared with the previous rate control algorithms for HEVC scalable extension.]]>1810202320391663<![CDATA[Sensing Matrix Optimization Based on Equiangular Tight Frames With Consideration of Sparse Representation Error]]>1810204020531537<![CDATA[Efficient Residual DPCM Using an <inline-formula> <img src="/images/tex/19187.gif" alt="l_1"> </inline-formula> Robust Linear Prediction in Screen Content Video Coding]]> optimization in the weight derivation by considering the statistical characteristics of graphical components in videos in an intracoding. Specifically we use the least absolute shrinkage and selection operator to derive the weights because the solution is accurate in high variance residue. Furthermore, we enhance parallelism in a line processing by restricting the support to the row-wise prediction to above samples or the column-wise prediction to the left samples. The proposed method uses an explicit RDPCM scheme, so a coding mode determined by rate-distortion optimization is transmitted to a decoder. For coding the overhead, we develop a context design in CABAC based on correlation between an intraprediction direction and an RDPCM prediction mode. It is demonstrated with the experimental results that the proposed method provides a significant coding gain over the state-of-the-art reference codec for screen content video coding.]]>181020542065874<![CDATA[Learning Cascaded Deep Auto-Encoder Networks for Face Alignment]]>1810206620782026<![CDATA[Animal Detection From Highly Cluttered Natural Scenes Using Spatiotemporal Object Region Proposals and Patch Verification]]>1810207920921183<![CDATA[Background Subtraction Using Background Sets With Image- and Color-Space Reduction]]>181020932103980<![CDATA[Learning Blind Quality Evaluator for Stereoscopic Images Using Joint Sparse Representation]]>181021042114605<![CDATA[Filtering of Brand-Related Microblogs Using Social-Smooth Multiview Embedding]]>181021152126761<![CDATA[Corrections to “Cross-Modal Correlation Learning by Adaptive Hierarchical Semantic Aggregation” [Jun 16 1201-1216]]]>18102127212763<![CDATA[IEEE Transactions on Multimedia information for authors]]>18102128212960<![CDATA[IEEE International Conference on Multimedia and Expo (ICME) 2017]]>181021302130310<![CDATA[Introducing IEEE Collabratec]]>1810213121311912<![CDATA[Expand Your Network, Get Rewarded]]>1810213221321042<![CDATA[IEEE Transactions on Multimedia]]>1810C3C350