Efficient image retrieval using support vector machines and Bayesian relevance feedback

Content-based image retrieval is an active research area in image processing. Recently, many researchers have employed support vector machines (SVMs) for image retrieval research area. This paper presents a multiple support vector machines for image classification in the first stage; and then according to the user´s marked images, we use relevance feedback based on Bayesian methodology, which yields the posteriori of the images in the database; The retrieval system can repeated by user during the relevance feedback stage. Experimental results based on a set of Corel images demonstrate that the proposed system achieves high performance.