Texture classification based on discriminative features extracted in the frequency domain

Texture identification can be a key component in Content Based Image Retrieval systems. Although formal definitions of texture vary in the literature, it is commonly accepted that textures are naturally extracted and recognized as such by the human visual system, and that this analysis is performed in the frequency domain. In this work, a feature extraction method is presented which employs a discrete Fourier transform in the polar space, followed by a dimensionality reduction. Selected features are then processed with vector quantization for the supervised segmentation of images into uniformly textured regions. Experiments performed on a standard test suite show that this method compares favorably to the state-of-the-art and improves over previously studied frequency-domain based methods.