Abstract
In this paper, convenient color space selection issue in content-based image retrieval system for low-level feature mining is addressed by the exploration of color edge histogram feature extraction on HSV, YIQ, YUV, and YCbCr color spaces. Moreover, Haar wavelet transform is applied to reduce feature vector count for the beneficial to speed up the retrieval process, and then semantic retrieval is obtained via similarity metric. Retrieval accuracy of each color space is analyzed through the parameters such as precision, recall, and response time of the system. Experimental results show that HSV color space-based retrieval system averagely gives 5%, 18.3%, and 26% high retrieval than the YIQ, YUV, and YCbCr color spaces, respectively.
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