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With the vast amount of visual information produced in various digital formats, image retrieval is becoming one of the most important areas in many areas including the Internet, medical image browsing, and automatic face and feature recognition just to name few. This paper presents new supervised texture segmentation and classification technique based on combining features extracted from the discrete wavelet frames of an image with a nonlinear band generation algorithm and an orthogonal subspace projection operator (OSP). The algorithm is supervised and needs apriori information about the number and location of textures present in the composite texture training images. The OSP operator role is twofold: to extract a set of texture signature vectors each uniquely characterizing only one texture; after that, the texture segmentation process commences and the signature vectors are used to identify/mark textures in new images, essentially a pixel labeling process with all pixels of one texture having the same label. The preliminary simulation results show satisfactory classification and segmentation on a set of composite texture images while having good real time performance and moderate storage and computational requirements.