Effective browsing of image search results via diversified visual summarization by clustering and refining clusters

Abstract

The unprecedented growth in applications making use of digital images and multimedia references raised the requirement for image and topic search. Systematic processing of this information is a basic prerequisite for effective analysis of this information, as well as organization and management of it. Likewise, large collections of images are available on the Web, and many search engines provide the possibility of Web image searching based on the keywords. However, there are some problems in finding the desirable image according to the user needs. These problems include inexpressiveness of queries in description of user requirement, large number of irrelevant images to the intended search, lack of summarization, time-consuming review of overall images and lack of diversity. Clustering of image search results can be an efficient solution in solving these problems. In this work, several Folding-based algorithms have been proposed for clustering of image search results. In these algorithms, the efficiency, in comparison with Folding algorithm, is improved through a more effective selection of cluster’s representatives, fuzziness, weight and utilization of hierarchical algorithm preferences. In the represented system, in order to fit clusters with data more appropriately, an algorithm is proposed for refining the clusters. The proposed clustering causes a more convenient task in retrieval process for the user and also causes the efficient retrieval of images. According to the experiences, the proposed method improves the acceptable precision of image clustering.