CFP: VCIP'17 Special Session on Regularization Techniques for High-Dimensional Visual Data Processing and Analysis
IEEE Visual Communications and Image Processing (VCIP) 2017, St. Petersburg, Florida, USA. December 10-13, 2017 http://www.vcip2017.org/call-for-special-sessions/accepted-special-sessions The explosive growth of high-dimensional visual data in computer vision requires effective techniques to reveal the underlying low-dimensional structure and discover the latent knowledge. Over the past decades, a variety of representative methods are proposed for visual data modelling and analysis, including manifold learning, matrix factorization, subspace learning, sparse coding, and deep learning. However, they often suffer from unsatisfactory robustness and generalization ability, as well as poor theoretical interpretability. To this end, many regularization techniques have been developed and shown effective. Despite the promising progress, many problems remain unsolved, and both theoretical and technical developments are desirable to provide new insights and tools in modelling the complexity of real world visual data. This special session aims to provide a forum for researchers all over the world to discuss their works and recent advances in algorithms and applications for advanced regularization techniques in high dimensional visual data analysis. Papers addressing interesting real-world visual computing applications are especially encouraged. *Important Dates: Submission: May 26, 2017 Acceptance Notification: August 25, 2017 *Organizers: Zhangyang (Atlas) Wang, Texas A&M University, USA Xi Peng, Institute for Infocomm Research Agency for Science, Singapore Sheng Li, Northeastern University, USA
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