Image mining presents special characteristics due to the richness of data that an image can show. Effective evaluation of results of image mining by content requires that the user point of view is used on the performance parameters. Comparison among different mining by similarity systems is particularly challenging owing to the great variety of methods implemented to represent likeness and the dependence that the result present of the used image set. Other obstacle is lag of parameter for comparing experimental performance. In this paper we propose an evaluation framework for comparing the influence of THE distance function by image mining by color and also a way to mine an image from its name. Experiments with color similarity mining by quantization on color space and measure of likeness between a sample and the image results have been carried out to illustrate the proposed scheme. Important aspects of this type of mining are also described

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Color Based Image Segmentation Deviation Factor Image Comparison Clustering Text Based Mining