A semi-supervised active learning FSVM for content based image retrieval

Abstract

Relevance feedback (RF) schemes based on support vector machine (SVM) have been widely used in content-based image retrieval to bridge the semantic gap between low-level visual features and high-level human perception. However, the conventional SVM based RF uses only the labeled images for learning which gives rise to the small sample problem. In this paper, we proposed a method to alleviate the small sample problem in SVM based RF by using semi-supervised active learning algorithm which uses a large amount of unlabeled data together with labeled data to build better models. In relevance feedback, active learning is often used to alleviate the burden of labeling by selecting only the most informative data. In addition, a semi-supervised approach has been developed which employs Nearest-Neighbor technique to label the unlabeled data with a certain degree of uncertainty in its class information. Using these automatically labeled samples, fuzzy support vector machine (FSVM) which takes into account the fuzzy nature of some training samples during its training is trained. We compared our method with standard active SVM based RF on a database of 10,000 images, the experiment results show that our method has a better performance and prove that it is an effective algorithm for CBIR. © 2013 Springer-Verlag.