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| Sparse Coding Model Based on Kernel Laplacian for Image Classification |
| LIU Yue1, PENG Hong-Jing1, QIAN Su-Jing1, SHI Wei2 |
1.College of Electronics and Information Engineering, Nanjing Tech University, Nanjing 211816
2.Department of Computer Science, Nanjing Institute of Mechatronic Technology, Nanjing 211135 |
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Abstract In bag-of-words with sparse coding model, similar features can be encoded as various sparse coding combinations due to the over-completeness of the codebook, which results in totally different visual words. In this paper, a sparse coding method based on kernel Laplacian for image classification is proposed. Firstly, a Laplacian matrix is constructed to capture geometric dependencies between the features in high-dimensional kernel space, and thus the similarity of sparse coding between the similar features can be maximally preserved. Secondly, the objective function is optimized for codebook learning by fixing codebook and sparse matrix alternately, and feature-sign search algorithm is used for sparse coding of the features. Finally, the one-to-all linear SVM classifier is applied to classify images. The experimental results on several datasets show the proposed algorithm decreases the quantization error dramatically and improves the classification performance.
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Received: 21 February 2013
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2014, Vol. 27 
