Multiple kernel active learning for image classification

Recently, multiple kernel learning (MKL) methods have shown promising performance in image classification. As a sort of supervised learning, training MKL-based classifiers relies on selecting and annotating extensive dataset. In general, we have to manually label large amount of samples to achieve desirable MKL-based classifiers. Moreover, MKL also suffers a great computational cost on kernel computation and parameter optimization. In this paper, we propose a local adaptive active learning (LA-AL) method to reduce the labeling and computational cost by selecting the most informative training samples. LA-AL adopts a top-down (or global-local) strategy for locating and searching informative samples. Uncertain samples are first clustered into groups, and then informative samples are consequently selected via inter-group and intra-group competitions. Experiments over COREL-5K show that the proposed LA-AL method can significantly reduce the demand of sample labeling and have achieved the state-of-the-art performance.