OCRM: Optimal Cost Region Matching Similarity Measure for Region Based Image Retrieval

Abstract

Content Based Image Retrieval (CBIR) has been the most significant area in the applications of Pattern recognition and Computer Vision for the last three decades. However, there are many open problems left unresolved. Among these, one of the current problems of CBIR is to obtain an effective Similarity Measure. The CBIR systems make use of Integrated Region Matching (IRM) to match segmented images which is computationally economic, but it is not a metric distance whereas systems that use Minimum Cost Region Matching (MiCRoM) as a similarity measure is a metric distance, but computationally expensive. In order to address the above problem, this paper has developed the Optimal Cost Region Matching (OCRM) similarity measure for region based image retrieval. The proposed OCRM uses the north-west corner rule of the Transportation problem that fulfills the monge property. The experiment carried out on 1000 color images taken from the Corel database that are compared with IRM, and MiCRoM similarity measures.

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Cite this paper

@inproceedings{Rao2014OCRMOC, title={OCRM: Optimal Cost Region Matching Similarity Measure for Region Based Image Retrieval}, author={N. Gnaneswara Rao and T. D. Sravani and V. Ravi Kumar}, year={2014} }