Online Content-Based Image Retrieval Using Active Learning

Abstract

Content-based image retrieval (CBIR) has attracted a lot of interest in recent years. When considering visual information retrieval in image databases, many difficulties arise. Learning is definitively considered as a very interesting issue to boost the efficiency of information retrieval systems. Different strategies, such as offline supervised learning or semi-supervised learning, have been proposed. Active learning methods have been considered with an increased interest in the statistical learning community. Initially developed in a classification framework, a lot of extensions are now proposed to handle multimedia applications. The purpose of this chapter is to present an overview of the online image retrieval systems based on supervised classification techniques. This chapter also provides algorithms in a statistical framework to extend active learning strategies for online content-based image retrieval.