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A novel content based image retrieval system using K-means/KNN with feature extraction

Chang Ray-I (Dept. of Engineering Science and Ocean Engineering National Taiwan University Taipei, Taiwan (R.O.C))
Lin Shu-Yu (Dept. of Engineering Science and Ocean Engineering National Taiwan University Taipei, Taiwan (R.O.C) + Research Center for Information Technology Innovation Academia Sinica Taipei, Taiwan (R.O.C))
Ho Jan-Ming (Research Center for Information Technology Innovation Academia Sinica Taipei, Taiwan (R.O.C))
Fann Chi-Wen (Research Center for Information Technology Innovation Academia Sinica Taipei, Taiwan (R.O.C))
Wang Yu-Chun (Research Center for Information Technology Innovation Academia Sinica Taipei, Taiwan (R.O.C))

Image retrieval has been popular for several years. There are different system designs for content based image retrieval (CBIR) system. This paper propose a novel system architecture for CBIR system which combines techniques include content-based image and color analysis, as well as data mining techniques. To our best knowledge, this is the first time to propose segmentation and grid module, feature extraction module, K-means and k-nearest neighbor clustering algorithms and bring in the neighborhood module to build the CBIR system. Concept of neighborhood color analysis module which also recognizes the side of every grids of image is first contributed in this paper. The results show the CBIR systems performs well in the training and it also indicates there contains many interested issue to be optimized in the query stage of image retrieval.

Keywords: content based images retrieval, K-means clustering, feature extraction, image retrieval