In this paper, we study computational models and techniques to combine textural and image features for classification of images on Internet. A framework is given to index images on the basis of textural, pictorial and composite information. The scheme makes use of weighted document terms and color invariant image features to obtain a high-dimensional similarity descriptor to be used as an index. Based on supervised learning, the k-nearest neighbor classifier is used to organize images into semantically meaningful groups of Internet images. Internet images are first classified into photographical and synthetical images. After classifying images into photographical and synthetical images, we further classify photographical images into portraits and non-portraits. Further, synthetical images are classified into button and non-button images.
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