Compressed domain feature transformation using evolutionary strategies for image classification
Recently, a number of approaches have been proposed which use compressed domain features for image retrieval and classification. While the main motivation of these approaches is to improve processing efficiency and reduce computational requirements, we propose a method which also aims at enhancing the content characterization capabilities of the compressed domain features in addition to efficiency improvement. We model the compressed domain feature values as random variables and approximate their associated probability mass functions as histograms. We then transform these histograms in such a way that the resulting classification rate based on these transformed histograms is improved. With a large number of possible transformations, we adopt an evolutionary strategy (ES) to search for the. optimal one. Experiments show that our proposed approach is able to obtain a better classification rate while the efficiency advantage of using compressed domain features is retained.