Content Based Image Retrieval Using SVM for Relevance Feedback

Abstract

This research shows an advantage of interfacing Support Vector Machine (SVM) in Relevance Feedback (RF). SVM uses user’s feedback to provide interface between query by example image during content based image retrieval (CBIR). In CBIR, images are retrieved from image database using color feature but semantic gap is generated between color feature and human perception. As an example, different users have different relevant images for same example image, also same user has different relevant images at different time. These all problems are solved by Relevance Feedback. During retrieval process, user selects most relevant images and SVM learning results are used to update weights of preferences for relevant images. Priorities are given to the positive feedbacks that have larger distances to hyper plane determined by support vectors. Based on this SVM, new retrieval images are displayed to user. Experiment results show that proposed (RF using SVM) approach improvement results over CBIR results.