Mining in Navigation-Pattern using Content-Based Image Retrieval
K. Karthika1, C. Arunachal Aperumal2
1K.Karthika, (M.E), Department of PG Studies & Engineering, S.A Engineering College, Anna University of Technology, Chennai (Tamil Nadu), India.
2C.Arunachalaperumal, M.E, (Ph.D), Department of PG Studies &Engineering, S.A Engineering college, Anna University of Technology, Chennai (Tamil Nadu), India.
Manuscript received on 18 June 2012 | Revised Manuscript received on 25 June 2012 | Manuscript published on 30 June 2012 | PP: 22-25 | Volume-1 Issue-2, June 2012 | Retrieval Number: B0163041212/2012©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Research has been devoted in the past few years to relevance Feedback as an effective solution to improve performance of Content-based image retrieval (CBIR). In this paper, we propose a color image pattern for further use, which reduce the iteration og image. To achieve the high efficiency and effectiveness of CBIR we are using two type of methods for feature extraction like SVM (support vector machine) and NPRF (navigation-pattern based relevance feedback).By using svm classifier as a category predictor of query and database images, they are exploited at first to filter out irrelevant images by its different low-level, concept and key point-based features. Thus we may reduce the size of query search in the db and enhanced by using texture based in which we combine GLCM and CCM.
Keywords: GLCM, CCM, SVM, Content Based Image Retrieval.
Scope of the Article: Image Processing