Image classification by multi-kernel dictionary learning

Recent studies have indicated the efficacy of selecting and combining the salient features from a pool of feature types in image retrieval and classification applications. In contrast to previous work, in this paper, we approach this problem as a selection and combination of the salient feature type(s) from a pool of feature types rather than selecting an individual feature. Our approach utilizes multiple kernels within the dictionary-learning framework where a combination of dictionary atoms represents individual categories. The category specific feature combination parameters or weights for kernel combination are determined by the mutual information techniques. The method is compared to a meta-algorithm for feature nomination. The multi-kernel dictionary learning method yields, on average, a 10% increase in classification accuracy with respect to the meta-algorithm in our preliminary experiments.