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Deep Sparse and Low-Rank Analytics for Robust Visual Representation

Deep Sparse and Low-Rank Analytics for Robust Visual Representation

Journal
Impact Score 4.62

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Special Issue Information

Submission Deadline: 20-10-2020
Journal Impact Score: 4.62
Journal Name: Journal of Visual Communication and Image Representation
Publisher: Journal of Visual Communication and Image Representation

Special Issue Call for Papers


Sparse/low-rank analytics and representation have been emerging as important topics for robust image processing and visual representation due to their great success to image restoration, de-noising and classification, etc. Although recent decade has witnessed lots of efforts on the study of sparse/low-rank analytics, and significant progress were made to improve the representation ability, some issues still remain unsolved.



For example, sparse/low-rank representation algorithms usually utilize the single-layer structures, so they fail to obtain the deep representations with more useful and valuable hidden hierarchical information discovered. With the fast development of deep learning and deep neural networks, it will be helpful to propose the deep/multi-layer sparse and low-rank representation frameworks for robust visual representation. It is known that both deep learning and sparse/low-rank coding are powerful representation learning systems based on different mechanisms and principles, but how to integrate them to improve the performance is still unclear and noteworthy exploring, which is the main goal of this special issue.



Although certain efforts have been made to incorporate the deep neural networks into sparse/low-rank analytics, most designs of so-called deep frameworks are still less straightforward. For example, some algorithms use deep features of deep models for sparse/low-rank analytics, or perform the sparse/low-rank analytics firstly and use the recovered data for deep learning. Although certain deep features or representations can be obtained by this kind of deep sparse/low-rank analytics, they still suffer from some drawbacks.



For example, they only simply add together multiple shallow sparse/low-rank coding layers, so current models still cannot produce accurate representations of visual data. Thus, it is now necessary to explore advanced integrated deep sparse/low-rank coding algorithms and theories for robust visual representation.



In this special issue, we solicit original research papers from diverse research fields, developing new deep sparse/low-rank analytics model for representing and understanding visual data, which aims to reduce the gap between sparse/low-rank coding and deep learning.



The topics of interest include, but are not limited to:



Survey/vision/review of sparse/low-rank visual analytics



Deep/multi-layer sparse coding or low-rank coding



Relations between sparse/low-rank coding and deep learning



Deep representation learning



Deep sparse or low-rank coding neural network



Convolutional sparse/low-rank coding



Robust sparse/low-rank subspace discovery



Theory and optimization for deep representation learning



Applications to robust image processing (e.g., restoration and de-noising) and recognition



Important Dates:



Paper submission due: October 20, 2020



First notification: November 20, 2020



Final decision made on all manuscripts: March 20, 2021



Managing Guest Editor:



Prof. Zhao Zhang, School of Computer and Information (School of Artificial Intelligence), Hefei University of Technology, China



Other Guest Editors:




  1. Dr. Paris Giampouras, Mathematical Institute of Data Science, Johns Hopkins University, USA


  2. Dr. Sheng Li, Department of Computer Science, University of Georgia, USA


  3. Prof. Shuicheng Yan, Department of Electrical and Computer Engineering, National University of Singapore, Singapore




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