Discriminative Features for Image Classification and Retrieval

In this paper, we present a new method to improve the performance of current bag-of-word based image classification process. After feature extraction, we introduce a pair wise image matching scheme to select the discriminative features. Only the label information from the raining-sets is used to update the feature weights via an iterative matching processing. The selected features correspond to the foreground content of the images thus highlight the high level category knowledge of images. "Visual words" are constructed on these selected features. Our method can be used as a refinement step for current image classification and retrieval process. We prove the efficiency of our methods in three tasks: supervised image classification, semi-supervised image classification and image retrieval. In the experiment part, we use two canonical datasets Caltech 256 and MSRC-v2 to validate our method. The results show that the share of discriminative features increases significantly to 87% by applying our selection. In four group of contract tests, supervised classification accuracies are shown to improve up to 21% while semi supervised classification accuracies are 15%. Image retrieval precision is also remarkable enhanced by our method.