Rotation-invariant and scale-invariant Gabor features for texture image retrieval

摘要

Conventional Gabor representation and its extracted features often yield a fairly poor performance in retrieving the rotated and scaled versions of the texture image under query. To address this issue, existing methods exploit multiple stages of transformations for making rotation and/or scaling being invariant at the expense of high computational complexity and degraded retrieval performance. The latter is mainly due to the lost of image details after multiple transformations. In this paper, a rotation-invariant and a scale-invariant Gabor representations are proposed, where each representation only requires few summations on the conventional Gabor filter impulse responses. The optimum setting of the orientation parameter and scale parameter is experimentally determined over the Brodatz and MPEG-7 texture databases. Features are then extracted from these new representations for conducting rotation-invariant or scale-invariant texture image retrieval. Since the dimension of the new feature space is much reduced, this leads to a much smaller metadata storage space and faster on-line computation on the similarity measurement. Simulation results clearly show that our proposed invariant Gabor representations and their extracted invariant features significantly outperform the conventional Gabor representation approach for rotation-invariant and scale-invariant texture image retrieval.