Fuzzy SVM Ensembles for Relevance Feedback in Image Retrieval

Relevance feedback has been integrated into content-based retrieval systems to overcome the semantic gap problem. Recently, Support Vector Machines (SVMs) have been widely used to learn the users’ semantic query concept from users’ feedback. The feedback is either ‘relevant’ or ‘irrelevant’ which forces the users to make a binary decision during each retrieval iteration. However, human’s perception of visual content is quite subjective and therefore, the notion of whether or not an image is relevant is rather vague and hard to define. Part of the small training samples problem faced by traditional SVMs can be thought of as the result of strict binary decision-making. In this paper, we propose a Fuzzy SVM technique to overcome the small sample problem. Using Fuzzy SVM, each sample can be assigned a fuzzy membership to model users’ feedback gradually from ‘irrelevant’ to ‘relevant’ instead of strict binary labeling. We also propose to use Fuzzy SVM ensembles to further improve the classification results. We conduct extensive experiments to evaluate the performance of our proposed algorithm. Compared to the experimental results using traditional SVMs, we demonstrate that our proposed approach can significantly improve the retrieval performance of semantic image retrieval.