[visionlist] CFP: [Deadline Extended] VCIP'17 Special Session on Regularization Techniques for High-Dimensional Visual Data Processing and Analysis

Sheng Li lisheng1989 at gmail.com
Thu May 25 19:41:16 -04 2017


[Apologies for cross-posting]
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CFP: [Deadline Extended] 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

[The deadline has been extended to June 5, 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: *June 5*, 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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