Sparse representation for image classification

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Yufang Tang

Shandong Normal University, China

: J Comput Eng Inf Technol

Abstract

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As a new theory of signal sampling, sparse representation derived from compressed sensing, which is obviously different from Nyquist sampling theory. More and more image classification methods based on sparse representation have been proved to be effectively used in different fields, such as face recognition, hyper spectral image classification, handwriting recognition, medical image processing, etc. Image classification methods based on sparse representation has become a hotspot of research topic in recent years. Not only the research institutes, but also the governments and the militaries have invested lots of energy and finance in this attractive task. In this presentation, we intend to review its history and development tendency, and reveal our latest research progress on sparse representation for image classification.

Biography

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Yufang Tang has been a Lecturer at School of Communication of Shandong Normal University in China since 2015. He obtained his Bachelor’s degree in Computer Science and Technology (2007) and Master’s degree in Computer Application Technology (2010) at Shandong Normal University, and received his Doctorate degree in Signal and Information Processing at Beijing University of Posts and Telecommunications (2015). He is engaged in the research on Computer Vision, Machine Learning, Artificial Intelligence and Data Mining, etc.

Email: tangyufang@sdnu.edu.cn