An adaptive image retrieval system with relevance feedback and clustering

Abstract

The objective for this paper is to develop an adaptive image retrieval system with an innovative approach to use artificial neural networks and clustering techniques to retrieve images similar to the input image. This paper involves retrieving images from huge image databases which are visually similar to a query image. Due to the enormous increase in image database size, and its high usage in a variety of applications, need for the development of Content Based Image Retrieval arose. It summarizes the problem, the proposed solution, and the desired results. The system uses neural networks, relevance feedback and clustering. The system is made intelligent by making the system learn the user's preference as feedback. The relevance feedback is used to improve the precision of the system by analyzing user's relevance feedback for each retrieved image while neural network and clustering techniques are used to reduce the time complexity of the system. The system uses three-layered neural network to train the system using image clusters as training dataset by a supervised approach. Also after taking the feedback from the user, the image clusters are re-clustered by rearranging the images after a successful retrieval. Given a user query as an image, the neural system retrieves related images by computing similarities with images in the given image clusters. To provide preference, from all the retrieved images user selects an image as relevant one and all other are hence treated as irrelevant ones. So, the rank of the selected image is increased while the ranks of other images are decreased. With this feedback, the system refinement method estimates global approximations and adjusts the similarity probabilities. © 2012 Springer-Verlag.