Chen-Kuo Chiang, Chih-Hsueh Duan, Shang-Hong Lai, Shih-Fu Chang. Learning Component-Level Sparse Representation Using Histogram Information for Image Classification. In International Conference on Computer Vision (ICCV), 2011.
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A novel component-level dictionary learning framework which exploits image group characteristics within sparse coding is introduced in this work. Unlike previous methods, which select the dictionaries that best reconstruct the data, we present an energy minimization formulation that jointly optimizes the learning of both sparse dictionary and component level importance within one uni.ed framework to give a discriminative representation for image groups. The importance measures how well each feature component represents the image group property with the dictionary by using histogram information. Then, dictionaries are updated iteratively to reduce the in.uence of unimportant components, thus re.ning the sparse representation for each image group. In the end, by keeping the top K important components, a compact representation is derived for the sparse coding dictionary. Experimental results on several public datasets are shown to demonstrate the superior performance of the proposed algorithm compared to thestate-of-the-art methods
@InProceedings{ICCV11:SparseClassification,
Author = {Chiang, Chen-Kuo and Duan, Chih-Hsueh and Lai, Shang-Hong and Chang, Shih-Fu},
Title = {Learning Component-Level Sparse Representation Using Histogram Information for Image Classification},
BookTitle = {International Conference on Computer Vision (ICCV)},
Year = {2011}
}
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