Efficient and accurate independent component filter-based features for texure similarity

This paper evaluates the accuracy and efficiency of collection-specific texture features for Content-Based Image Retrieval. Independent Component Analysis is used to extract Independent Component Filters (ICF) from an image set. As these ICF are learned from the image set, the hypothesis is that they should provide texture features that are more effective than those extracted using generic filter banks. We describe a method for extracting candidate ICF from an image set, and choosing a representative subset from them. These are then used to extract image features. A simple CBIR system has been developed to evaluate the performance of these features on two standard texture image collections, compared with features extracted using multiple banks of Gabor filters. The results indicate that ICF-based features perform better than Gabor-based features, even when a much smaller number of ICF features is used than Gabor features. The ICF features are thus more accurate, and more efficient.