3D Color Feature Extraction in Content-Based Image Retrieval
A.Komali1, V.Satish Kumar2, K.Ganapathi Babu3, A.S.K.Ratnam4
1A.Komali, pursuing M.Tech in Computer Science Engineering at Vignan’s LARA Institute Of Technology and Sceince, Vadlamudi, Guntur Dist., A.P., India.
2V.Satish Kumar, pursuing M.Tech in Computer Science Engineering at Vignan’s LARA Institute Of Technology and Sceince, Vadlamudi, Guntur Dist., A.P., India.
3K.Ganapathi Babu, pursuing M.Tech in Computer Science Engineering) at Vignan’s LARA Institute Of Technology and Sceince, Vadlamudi, Guntur Dist., A.P., India
4A.S.K.Ratnam, Head, Department of CSE, Vignan’s LARA Institute Of Technology & Sceince,Vadlamudi Guntur Dist., A.P., India.
Manuscript received on April 04, 2013. | Revised Manuscript received on April 28, 2013. | Manuscript published on May 05, 2013. | PP: 560-563 | Volume-3, Issue-2, May 2013. | Retrieval Number: C0749062312/2013©BEIESP
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© The Authors. Published By: 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: Content-based Image Retrieval (CBIR) consists of retrieving the most visually similar images to a given query image from a database of images. “Content-based” means that the search will analyze the actual contents of the image rather than the metadata such as keywords, tags, and/or descriptions associated with the image. The term ‘content’ in this context might refer to colours, shapes, textures, or any other information that can be derived from the image itself. CBIR is desirable because most web based image search engines rely purely on metadata and this produces a lot of garbage in the results. Also having humans manually enter keywords for images in a large database can be inefficient, expensive and may not capture every keyword that describes the image. Thus a system that can filter images based on their content would provide better indexing and return more accurate results. This paper proposes 3D colour feature extraction for comparing the contents
Keywords: QBIC (Query by Image Content), CBVIR (Content Based Visual Information Retrieval), Color Space, Texture, Conventional Color Histogram , CMY, HSV.