Group sparse representation of adaptive sub-domain selection for image classification

Recent years have seen an increasing interest in codebook-based model(bag-of-words-BOW) for image representation, which includes the basic bag-of-words model and its improved version for local descriptor reconstruction with sparse coding (SC) and locality-constrained linear coding (LLC) etc. Although the recent coding strategies in the BoW model can lead to prospect performance using large amounts of codes (codebooks) for image classification, it is usually computational expensive for obtaining the global image representation through calculating the similarities between each local descriptor and all codes. Therefore, this study proposes to represent a local descriptor with an adaptive code or its variation modes (adaptive subdomain) in a small set of codebooks. The proposed strategy can adaptively select one code to saliently representation, or adaptively select one sub-domain of a code for group sparse reconstruction of a local descriptor. Due to computational cost mainly on the similarity calculation between local descriptors and the predefined codebooks, our proposed strategy using small set of codebook can greatly reduce computational time, and in addition, shows prospect performances for image classification on an scene database, called OM-RON scene dataset, and the benchmark data: 15 natural scene dataset.