Abstract
In recent years, sparse coding has been used in a wide range of applications including classification and recognition. Different from many other applications, the sparsity pattern of features in many classification tasks are structured and constrained in some feasible domain. In this paper, we proposed a re-weighted \(\ell _{2,1}\) norm based structured sparse coding method to exploit such structures in the context of classification and recognition. In the proposed method, the dictionary is learned by imposing the class-specific structured sparsity on the sparse codes associated with each category, which can bring noticeable improvement on the discriminability of sparse codes. An alternating iterative algorithm is presented for the proposed sparse coding scheme. We evaluated our method by applying it to several image classification tasks. The experiments showed the improvement of the proposed structured sparse coding method over several existing discriminative sparse coding methods on tested data sets.
Yong Xu would like to thank the supports by National Nature Science Foundations of China (61273255 and 61070091), Engineering and Technology Research Center of Guangdong Province for Big Data Analysis and Processing ([2013]1589-1-11), Project of High Level Talents in Higher Institution of Guangdong Province (2013-2050205-47) and Guangdong Technological Innovation Project (2013KJCX0010). Yuping Sun would like to thank the support by China Scholarship Council Program. Yuhui Quan and Yu Luo would like to thank the partial support by Singapore MOE Research Grant R-146-000-178-112.
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Xu, Y., Sun, Y., Quan, Y., Luo, Y. (2015). Structured Sparse Coding for Classification via Reweighted \(\ell _{2,1}\) Minimization. In: Zha, H., Chen, X., Wang, L., Miao, Q. (eds) Computer Vision. CCCV 2015. Communications in Computer and Information Science, vol 546. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48558-3_19
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