Graph Cut Based Local Binary Patterns for Content Based Image Retrieval

Abstract

In this paper, a new algorithm which is based on the graph cut theory and local binary patterns (LBP) for content based image retrieval (CBIR) is proposed. In graph cut theory, each node is compared with the all other nodes for edge map generation. The same concept is utilized at LBP calculation which is generating nine LBP patterns from a given 3—3 pattern. Finally, nine LBP histograms are calculated which are used as a feature vector for image retrieval. Two experiments have been carried out for proving the worth of our algorithm. It is further mentioned that the database considered for experiments are Brodatz database (DB1), and MIT VisTex database (DB2). The results after being investigated shows a significant improvement in terms of their evaluation measures as compared to LBP and other existing transform domain techniques.

Keywords: Feature Extraction; Local Binary Patterns; Image Retrieval