A fuzzy feature clustering with relevance feedback approach to content-based image retrieval

Abstract

The increasing number of digitized images required an efficient image retrieval system. In this paper, we demonstrate the fundamental principles, implementation methods, performance evaluations, and experimental results from the proposed model. We present a region-based prototype image retrieval system named FuzzyImage. The system is characterized by feature vectors. First, we segment an image into regions depending on clustering similar feature vectors by fuzzy c-means. Next, a similar measurement is used to evaluate the similarity between the query image and incorporated regions. The users can select the most interesting regions from 5 sample images that pop-up, and by feedback to the system. Based on the selected individual regions of query images, the overall similarity helps filter out irrelevant images in a database after relevance feedback and enables a simple user-oriented query interface for a region-based image retrieval system. This algorithm is implemented and tested on general-purpose images. This project makes three main contributions to a region-based CBIR system. First, a region segmentation method is employed in the FuzzyImage system. Second, this system takes the user's intuition into consideration and designs a user-oriented interface to directly search the database. Thirdly, we evaluate retrieval precision of the system to support this theoretical claim.