Supervised Segmentation Based on Texture Signatures Extracted in the Frequency Domain

Authors:
Antonella Lillo;Giovanni Motta;James A. Storer
Affiliations:
Brandeis University, Waltham, MA 02453,;Bitfone Corp., Laguna Niguel, CA 92677,;Brandeis University, Waltham, MA 02453,
Venue:
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Year:
2007

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Abstract

Texture identification can be a key component in Content Based Image Recognition 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. The method presented here employs a discrete Fourier transform in the polar space to extract features, which are then classified with a vector quantizer for supervised segmentation of images into texture regions. Experiments are conducted on a standard database of test problems that show this method compares favorably with the state-of-the-art and improves over previously proposed frequency-based methods.