Local Rotation Pattern: A Local Descriptor of Color Textures

Hela Jebali, Noël Richard, Mohamed Naouai


Describing color textures is an extremely challenging problem in pattern recognition and computer vision. In this paper, a new texture feature is proposed and investigated for color texture image classification. Based on quaternion representation of color images and quaternion rotation, a Local Rotation Pattern descriptor (LRP) is proposed. Using quaternion to represent color images is done by encoding the three RGB channels into the three imaginary parts of a quaternion. The distance between two color can be expressed as the angle of rotation between two unit quaternions using the geodesic distance to obtain finally our LRP histograms. Performance in texture classification is assessed for three challenging datasets: Vistex, Outex-TC13 and USPtex databases facing to the recent results from the state-of-the-art. Results show the high efficiency of the proposed approach.


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