The CBIR problem is motivated by the need to search the exponentially increasing space of image and image databases efficiently and effectively. The survey feature extraction and selection techniques adopted in content based image retrieval (CBIR), is a technique that uses the visual content of a still image to search for similar images in large scale image databases, according to a user’s interest. The visual content of an image is analyzed primarily in terms of low level features extracted from the image which constitute color, texture and shape features. A novel color image retrieval method using both color and local spatial feature histograms (CLSFH) is proposed in this paper. In CLSFH, the non uniform quantized HSV color model is used, the mean, the standard deviation of 5x5 neighbor of every pixel are calculated, and are used to generate the Local Mean Histogram, the Local Standard Deviation Histogram; is defined and computed using the proposed algorithm. A relevance feedback approach that attempts to bridge the gap between low level features extracted from an image and high level semantic features. By integrating the user feedback information, the feature selection is able to bridge the gap between low-level visual features and high-level semantic information, leading to the improved image retrieval accuracy.

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