Feature Extraction for Image Retrieval Using Color Spaces and GLCM
Madhura C1, Dheeraj D2

1Madhura C, Department of Information Science and Engineering, P.E.S Institute of Technology, Bangalore (Karnataka), India.
2Dheeraj D, Assistant Professor, Department of Information Science and Engineering, P.E.S Institute of Technology, Bangalore (Karnataka), India.
Manuscript received on 10 July 2013 | Revised Manuscript received on 18 July 2013 | Manuscript Published on 30 July 2013 | PP: 159-162 | Volume-3 Issue-2, July 2013 | Retrieval Number: B1035073213/13©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: Due to the enormous increase in the size of image databases as well as its vast deployment in various applications, the need for Content Based Image Retrieval (CBIR) development arose. This paper describes a hybrid feature extraction approach of our research and solution to the problem of designing a CBIR system manually. Two features are used for retrieving the images such as color and texture. Color feature is extracted by using different color space such as RGB, HSV and YCbCr. Texture feature is extracted by applying Gray Level Co-occurrence Matrix(GLCM). The image is retrieved by combining color and texture feature and the color space which gives the best result as analyzed using precision and recall graph.
Keywords: Color Spaces, Euclidean Distance, Image Retrieval, Precision, Recall.

Scope of the Article: Image analysis and Processing