Multiresolution Rotation-Invariant Texture Classification Using Feature Extraction in the Frequency Domain and Vector Quantization

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. The vast majority of the methods proposed in the literature provide good characterization of texture in controlled environments. In order to better describe textures, features must capture the nature of the texture, invariant to rotational, shift, and scale transformations. In this work, a rotation-invariant feature extraction technique is presented, extending our previous work (A. Di Lillo et al., 2007), which was not rotation-invariant. The technique demonstrated here similarly employs a discrete Fourier transform in the polar space followed by a dimensionality reduction, but achieves rotational invariance by incorporating an additional transform into the process. Selected features are then processed with vector quantization for the classification of textures. Experiments performed on a standard test suite show that this method improves over previous methods.