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Local Features Are Not Lonely - Laplacian Sparse Coding for Image Classification

9 years 11 months ago
Local Features Are Not Lonely - Laplacian Sparse Coding for Image Classification
Sparse coding which encodes the original signal in a sparse signal space, has shown its state-of-the-art performance in the visual codebook generation and feature quantization process of BoW based image representation. However, in the feature quantization process of sparse coding, some similar local features may be quantized into different visual words of the codebook due to the sensitiveness of quantization. In this paper, to alleviate the impact of this problem, we propose a Laplacian sparse coding method, which will exploit the dependence among the local features. Specifically, we propose to use histogram intersection based kNN method to construct a Laplacian matrix, which can well characterize the similarity of local features. In addition, we incorporate this Laplacian matrix into the objective function of sparse coding to preserve the consistence in sparse representation of similar local features. Comprehensive experimental results show that our method achieves or outperforms exi...
Shenghua Gao, Wai-Hung Tsang, Liang-Tien Chia, Pei
Added 02 Aug 2010
Updated 02 Aug 2010
Type Conference
Year 2010
Where CVPR
Authors Shenghua Gao, Wai-Hung Tsang, Liang-Tien Chia, Peilin Zhao
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