Find dominant bins of a histogram by sparse representation

Bag of words (BoW) method has been widely used for image (feature) representation and gained great success for its simplicity but efficient power. However, due to the unsupervised clustering, visual words are equally treated for all classes and are not discriminative for classification. We found that only a few words are activated when samples from one class are sparsely represented over the visual words. Based on this observation, we propose an approach to find the dominant and useful bins in image histogram for each class with sparse representation technique. The resulted histogram with only dominant bins then becomes more discriminative for classification. Experiments on three widely used datasets demonstrate superior performance of the proposed approach over standard BoW method.