Low-rank, sparse matrix decomposition and group sparse coding for image classification

This paper presents a novel image classification framework (referred to as LR-GSC) by leveraging the low-rank, sparse matrix decomposition and group sparse coding. First, motivated by the observation that local features (such as SIFT) extracted from neighboring patches in an image usually contain correlated (or common) items and specific (or noisy) items, we decompose the local features matrix of an image into a low-rank matrix and a sparse matrix. Second, we train the group sparse dictionaries on the low-rank parts and sparse parts respectively. And then, the dictionaries of the two parts are jointed to encode the original SIFT features by group coding. Finally, linear SVM classifier is used for the classification. The method is tested on the Caltech-101 dataset and UIUC-sports dataset, and achieves competitive or better results than the state-of-the-art methods.