An effective SVM-based active feedback framework for image retrieval

Abstract

Due to the time and hardware restriction, the amount of feedback information is limited in each image retrieval loop. To solve this problem, this paper proposes an effective relevance feedback (RF) method based on Support Vector Machine (SVM) framework, which increases the amount of feedback information by cluster analysis and utilizing unlabeled images to build SVM classifier. As a result, a pseudo-label strategy, consist of a feature subspace partition algorithm and a cluster analysis scheme, is proposed for unlabeled images selection. Experimental results demonstrate the relative high effectiveness of our proposed active framework.