Abstract
Recently support vector machines (SVM) using spatial pyramid matching (SPM) kernel have been highly successful in image classification applications. And linear spatial pyramid matching using sparse coding (ScSPM) scheme has been proposed to enhance the performance of SPM both in time and classification accuracy. In order to reduce the time complexity of dictionary construction process, sparse coding with affinity propagation method has been proposed in this paper. Because the dictionary used for sparse coding plays a key role in these methods, we also adopt supervised dictionary learning method to construct dictionary. The coding coefficients of each class have greater separability for SVM classification. Substantial experiments on Scene15 and CalTech101 image datasets have been conducted to investigate the performance of proposed approach in multi-class image classification; the results show that the approach can reach higher accuracy compared with ScSPM.
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Acknowledgments
The research work described in this paper was fully supported by the grants from the National Natural Science Foundation of China (Project No. 90820010, 60911130513). Prof. Ping Guo is the author to whom all correspondence should be addressed.
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This work is an extended version of the paper presented at the 2011 International Conference on Neural Information Processing (ICONIP) [1].
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Xu, B., Hu, R. & Guo, P. Combining affinity propagation with supervised dictionary learning for image classification. Neural Comput & Applic 22, 1301–1308 (2013). https://doi.org/10.1007/s00521-012-0957-7
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DOI: https://doi.org/10.1007/s00521-012-0957-7