Image clustering fusion technique based on BFS

Abstract

With the increasing in number and size of databases dedicated to the storage of visual content, the need for effective retrieval systems has become crucial. The proposed method makes a significant contribution to meet this need through a technique in which sets of clusters are fused together to create an unique and more significant set of clusters. The images are represented by some features and then are grouped by these features, that are considered one by one. A probability matrix is then built and explored by the breadth first search algorithm with the aim of select an unique set of clusters. Experimental results, obtained using two different datasets, show the effectiveness of the proposed technique. Furthermore, the proposed approach overcomes the drawback of tuning a set of parameters that fuse the similarity measurement obtained by each feature to get an overall similarity between two images.