Multi-view learning with batch mode active selection for image retrieval

With the explosive growth of Internet image data, labeling image data for image retrieval has become an increasingly onerous task. To that end, we proposed a novel multi-view learning with batch mode active learning framework, MV-BMAL, for improving the performance of image retrieval. Specifically, color, texture and shape features are extracted and considered as un-correlated and sufficient views of an image, then each classifier is trained on these views respectively, and the schema makes full use of the classification results of each unlabeled samples to find out the most informative and representative samples for automatically or manually labeling. Finally, we evaluate MV-BMAL on benchmark data sets, and the experimental results show that our proposed MV-BMAL algorithm significantly outperforms the previous methods.