Low-Rank Sparse Coding for Image Classification

Low-Rank Sparse Coding for Image Classification

T. Zhang, B. Ghanem, S. Liu, N. Ahuja
"Low-Rank Sparse Coding for Image Classification"
International Conference on Computer Vision (ICCV 2013)
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T. Zhang, B. Ghanem, S. Liu, N. Ahuja
Low Ranks, Coding, Image Classification
2013
In this paper, we propose a low-rank sparse coding (LRSC) method that exploits local structure information among features in an image for the purpose of image-level classification. LRSC represents densely sampled SIFT de- scriptors, in a spatial neighborhood, collectively as low-rank, sparse linear combinations of codewords. As such, it casts the feature coding problem as a low-rank matrix learning problem, which is different from previous methods that encode features independently. This LRSC has a number of attractive properties. 
(1) It encourages sparsity in feature codes, locality in codebook construction, and low-rankness for spatial consistency. 
(2) LRSC encodes local features jointly by considering their low-rank structure information, and is computationally attractive. We evaluate the LRSC by comparing its performance on a set of challenging bench- marks with that of 7 popular coding and other state-of-the- art methods. Our experiments show that by representing local features jointly, LRSC not only outperforms the state-of- the-art in classification accuracy but also improves the time complexity of methods that use a similar sparse linear repre- sentation model for feature coding.​