International Journal of Engineering and Technology

Colors in an image provides tremendous amount of information. Using this color information images can be segmented, analyzed, labeled and indexed. In content based image retrieval system, color is one of the basic primitive features used. In Prevalent Color Extraction and indexing, the most extensive color on an image is identified and it is used for indexing. For implementation, Asteroideae flower family image dataset is used. It consist of more than 16,000 species, among them nearly 100 species are considered and indexed by dominating colors. To extract the most appealable color from the user defined images, the overall color of an image has to be quantized. Spatially, quantizing the color of an image to extract the prevalent color is the major objective of this paper. A combination of K-Mean and Expectation Minimization clustering algorithm called hidden-value learned K-mean clustering quantization algorithm is used to avoid the over clustering behavior of K-Mean algorithm. The experimental result shows the marginal differences between these algorithms.