Discriminative Dictionary Learning Based on Supervised Feature Selection for Image Classification

The bag-of-features based models are widely used for image classification. In these models, an image is represented as a set of visual words which come from a dictionary. Therefore, a well learned dictionary is responsible for the discriminative power of representations of images. Our observations show that the representation of an image carries rich underlying information of a dictionary, so we propose a novel method to learn a dictionary by analyzing histogram representations of images, called Discriminative Dictionary Learning based on Supervised Feature Selection for Image Classification (DFS). Instead of directly learning a dictionary from the feature space, we construct a discriminative and compact dictionary from a coarse dictionary. The supervised feature selection technique is brought into the analysis of histogram representation, which eventually leads to dictionary refinement. Experimental results on challenging databases (Caltech-101, Caltech-256) show that learned dictionaries works better for bag-of-features based models.