Search results for key=MoS2011 : 1 match found.

This study aims to find more effective methods for collection-specific CBIR. A lot of work has been done in trying to adapt a system by user feedback, in this study we aim to adapt CBIR systems for specific image collections in an automated manner. Independent Component Analysis (ICA), a high order statistical technique, is used to extract Independent Component Filters (ICF) from image sets. As these filters are adapted to the data, the hypothesis is that they may provide features which are more effective for collection-specific CBIR. To test this question, this study develops a methodology to extract ICF from image sets and use them to extract filter responses. In developing this method, the study uses image cross-correlation and clustering to solve issues to do with shifted/duplicate filters and selecting a smaller set of filters to make CBIR practical. The method is used to generate filter responses for the VisTex database . The filter response energies are used as features in the GNU Image Finding Tool (GIFT). The experiments show that features extracted using ICF have the potential to improve the effectiveness of collection-specific CBIR, although some more work in this area is required.