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Rotation- and Scale-Invariant Texture Classification Using Log-Polar and Ridgelet Transforms

Mahersia H., Hamrouni K.

Machine Graphics and Vision

2009

Vol. 18, No. 2

215-232

Classification of distorted texture images is a challenging and important problem in real world image analysis and understanding. This paper proposes a new texture characterization method which is robust to geometric distortions, including rotation and scale changes. The rotation- and scale-invariant feature extraction for a given image involves applying the log-polar transform to eliminate the rotation and scale effects, followed by the ridgelet transform. In the experiments, the K-nearest neighborhood classifier is employed, using Euclidian and Manhattan distances to classify two sets of 30 and 40 distinct natural textures selected from the Brodatz and the VisTex albums. The experimental results, based on different test data sets for images with different orientations and scales, show that the proposed classification scheme using log-polar ridgelet signatures outperforms texture classification based on log-polar and wavelet transforms. Its overall accuracy rate reaches 100% for orientation or scale changes, and is about 73.708% for joint rotation and scale changes. These results demonstrate the effectiveness of our characterization method in texture image classification experiments.