Abstract The effectiveness of the image representation based on bag-of-visual words(BoW) model is majorly limited by the quantization error. To address this issue, an improved image representation based on multiple visual codebooks is proposed in this paper, which considers both visual codebook construction and feature coding. The proposed method specifically consists of 1) multiple visual codebooks construction, in which the compact and complementary visual codebooks are iteratively generated; 2) image representation, in which the visual words are firstly selected from each individual visual codebook, then the coding coefficients are determined by using the regularized linear regression method, and finally the image is represented by combining the spatial pyramid structure. The experimental results on several benchmark image classification datasets demonstrate the consistent and significant improvement of the proposed method.
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2013, Vol. 26 
