Abstract: Active learning plays an important role for boosting interactive image retrieval. Among various methods, support vector machine(SVM) based active learning approaches have been drawn substantial attention. However, most SVM-based active learning methods are challenged by small example problem, asymmetric distribution problem, and redundancy among examples. This paper proposed two mechanisms to tackle above problems; (1) designing an asymmetric semisupervised learning(ASL) framework that exploits unlabeled data for semantic relevant and irrelevant classes in different ways. Under the influence of ASI，the efficiency of SVM is significantly improved;and(2) developing a representative measure based active selection criterion to identify the most informative images from unlabeled data while the diversity among them is augmented. Experimental results validate the superiority of our scheme over several existing methods.

Key words: Image retrieval, Relevance feedback, Support vector machines, Semi-supervised learning, Active learning