Quadrant Motif Approach for Image Retrieval

Abstract

In this paper, we propose an image retrieval approach based on Quadrant Motif Scan (QMS). Motif scans from segmented blocks inside an image are the primary notion to extract image features. We exploit recursive quadrant segmentation in images and stratify hierarchical regions for matching comparison. Regions in the same stratum hold an identical credit, which is used for similarity metric. For the sake of matching flexibility, a dynamic adjustment scheme of credit setting is offered. In this sense, a user can arbitrarily adjust the credit parameters to pursue better retrieval results. Besides, a peak inspection technique is also added in the QMS matching metric to enhance performance. This means can helpfully refine retrieval performance with trivial computational cost. Experimental results reveal that effectiveness and efficiency of QMS are comparable to the Motif Cooccurrence Matrix (MCM) method while QMS is competent to deal with image scaling.