Image classification using label constrained sparse coding

Sparse coding has been widely used for feature encoding in recent years. However, the encoded parameters’ similarity is ignored with sparse coding. Besides, the label information from which class the local feature is extracted is also ignored. To solve this problem, in this paper, we propose a novel feature encoding method called label constrained sparse coding (LCSC) for visual representation. The visual similarities between local features are jointly considered with the corresponding label information of local features. This is achieved by combining the label constraints with the encoding of local features. In this way, we can ensure that similar local features with the same label are encoded with similar parameters. Local features with different labels are encoded with dissimilar parameters to increase the discriminative power of encoded parameters. Besides, instead of optimizing for the coding parameter of each local feature separately, we jointly encode the local features within one sub-region in the spatial pyramid way to combine the spatial and contextual information of local features. We apply this label constrained sparse coding technique for classification tasks on several public image datasets to evaluate its effectiveness. The experimental results shows the effectiveness of the proposed method.