Scale and Rotation Invariant Gabor Features for Texture Retrieval

Abstract

For image classification applications it is often useful to generate a compact representation of the texture of an image region. The conventional representation of image textures using extracted Gabor wavelet coefficients often yields poor performance when classifying scaled and rotated versions of image regions. In this paper we propose a scale and rotation invariant feature generation procedure for classification and of images using Gabor filter banks. Firstly, to obtain scale and rotation invariant features, each image is decomposed at different scales and orientations. Then, in order to create unique feature vectors, we apply a circular shift operation to both scale and rotation dimensions to shift the maximum value of the Gabor filters to the first orientation of the first scale and the energies of these filtered images are calculated. To demonstrate the effectiveness of our proposed approach we compare its performance with the most recent texture feature generation methods in a classification task. Experimental results show that our proposed feature generation method is more accurate at classifying scaled and rotated textures than the existing methods.