A HIERARCHICAL APPROACH TO ROTATION-INVARIANT TEXTURE FEATURE EXTRACTION BASED ON RADON TRANSFORM PARAMETERS

ABSTRACT In this paper, we propose an efficient hierarchical method for extracting invariant texture features using the Gabor wavelets and Radon transform parameters. The proposed method applies the Radon transform to estimate the directional information in the highband texture image extracted by Gabor wavelets. The directional information is then used to make the texture feature invariant to rotation. To show the efficiency of our scheme, we developed a texture-based image retrieval system based on the proposed method and evaluated it on a set of images from the Brodatz album. Experimental results show that the proposed system outperforms previous rotation-invariant systems significantly

Robust rotation-invariant texture classification using a model based approach

In this paper, a model based texture classification procedure is presented. The texture is modeled as the output of a linear system driven by a binary image. This latter retains the morphological characteristics of the texture and it is specified by its spatial autocorrelation function (ACF). We show that features extracted from the ACF of the binary excitation suffice to represent the texture for classification purposes. Specifically, we employ a moment invariants based technique to classify the ACF. The resulting proposed classification procedure is thus inherently rotation invariant. Moreover, it is robust with respect to additive noise. Experimental results show that this approach allows obtaining high correct rotation-invariant classification rates while containing the size of the feature space

Robust rotation-invariant texture classification using a model based approach

In this paper, a model based texture classification procedure is presented. The texture is modeled as the output of a linear system driven by a binary image. This latter retains the morphological characteristics of the texture and it is specified by its spatial autocorrelation function (ACF). We show that features extracted from the ACF of the binary excitation suffice to represent the texture for classification purposes. Specifically, we employ a moment invariants based technique to classify the ACF. The resulting proposed classification procedure is thus inherently rotation invariant. Moreover, it is robust with respect to additive noise. Experimental results show that this approach allows obtaining high correct rotation-invariant classification rates while containing the size of the feature space