SVM-based active feedback in image retrieval using clustering and unlabeled data

In content-based image retrieval, relevance feedback is studied extensively to narrow the gap between low-level image feature and high-level semantic concept. However, most methods are challenged by small sample size problem since users are usually not so patient to label a large number of training instances in the relevance feedback round. In this paper, this problem is solved by two strategies: (1) designing a new active selection criterion to select images for user's feedback. It takes both the informative and the representative measures into consideration, thus the diversities between these images are increased while their informative powers are kept. With this new criterion, more information gain can be obtained from the feedback images; and (2) incorporating unlabeled images within the co-training framework. Unlabeled data partially alleviates the training data scarcity problem, thus improves the efficiency of support vector machine (SVM) active learning. Systematic experimental results verify the superiority of our method over existing active learning methods.