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Self-explanatory Sparse Representation for Image Classification
Bao-Di Liu1, Yu-Xiong Wang2, Bin Shen3, Yu-Jin Zhang4, and Martial Hebert2
1Col. of Information and Control Engineering, China University of Petroleum, Qingdao 266580, China
thu.liubaodi@gmail.com
2Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA
yuxiongw@cs.cmu.edu
hebert@ri.cmu.edu
3Dept. of Computer Science, Purdue University, West Lafayette, IN 47907, USA
bshen@purdue.edu
4Dept. of Electronic Engineering, Tsinghua University, Beijing 100084, China
zhang-yj@mail.tsinghua.edu.cn
Abstract. Traditional sparse representation algorithms usually operate in a single Euclidean space. This paper leverages a self-explanatory reformulation of sparse representation, i.e., linking the learned dictionary atoms with the original feature spaces explicitly, to extend simultaneous dictionary learning and sparse coding into reproducing kernel Hilbert spaces (RKHS). The resulting single-view self-explanatory sparse representation (SSSR) is applicable to an arbitrary kernel space and has the nice property that the derivatives with respect to parameters of the coding are independent of the chosen kernel. With SSSR, multiple-view self-explanatory sparse representation (MSSR) is proposed to capture and combine various salient regions and structures from different kernel spaces. This is equivalent to learning a nonlinear structured dictionary, whose complexity is reduced by learning a set of smaller dictionary blocks via SSSR. SSSR and MSSR are then incorporated into a spatial pyramid matching framework and developed for image classification. Extensive experimental results on four benchmark datasets, including UIUC-Sports, Scene 15, Caltech-101, and Caltech-256, demonstrate the effectiveness of our proposed algorithm.
Keywords: Reproducing Kernel Hilbert Spaces, Sparse Representation, Multiple View, Image Classification
LNCS 8690, p. 600 ff.
lncs@springer.com
© Springer International Publishing Switzerland 2014