Abstract
Non-parametric subspace classifier, such as collaborative representation based classification, sparse representation based classification obtains superior performance to conventional parametric model method, such as support vector machine and softmax regression, for visual classification. Recently, a probabilistic collaborative representation based classifier, which utilizes a hybrid representation (shared representation and class specific representation) to a test sample, leading to state-of-the-art classification performance. However, the probabilistic collaborative representation based classification does not consider the nonlinear characteristics hidden in visual features. In the paper, we propose to utilize kernel technique to extend the probabilistic collaborative presentation based classification method. Experimental results on several benchmark datasets demonstrate that our propose method obtains favourable classification performance.
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Acknowledgment
This paper is supported partly by the National Natural Science Foundation of China (Grant No. 61402535, No. 61271407), the Natural Science Foundation for Youths of Shandong Province, China (Grant No. ZR2014FQ001), the Natural Science Foundation of Shandong Province (Grant No. ZR2017MF069), Qingdao Science and Technology Project (No. 17-1-1-8-jch), and the Fundamental Research Funds for the Central Universities, China University of Petroleum (East China) (Grant No. 16CX02060A), International S And T Cooperation Program of China (Grant No. 2015DFG12050).
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Meng, J., Wang, Y., Liu, BD. (2018). Probabilistic Collaborative Representation with Kernels for Visual Classification. In: Huet, B., Nie, L., Hong, R. (eds) Internet Multimedia Computing and Service. ICIMCS 2017. Communications in Computer and Information Science, vol 819. Springer, Singapore. https://doi.org/10.1007/978-981-10-8530-7_38
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DOI: https://doi.org/10.1007/978-981-10-8530-7_38
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