A very fundamental issue in designing a content-based image retrieval system is to select the image features that best represent the image contents in a database. Such a selection requires a comprehensive evaluation of retrieval performance of image features. In this paper, we provide a detailed comparison of a number of commonly used color and texture features based on a large and diverse collection of image data. The investigated color features include color histograms, color moments, color coherence vectors and color correlogram with respect to different color spaces and quantizations. As for texture features, we compare Tamura features, edge histograms, MRSAR, Gabor texture feature, mid wavelet transform features. The result of this experiment can be used as a benchmark for selecting features in a content-based image retrieval system.
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