Invariant texture retrieval using modified Zernike moments

摘要

This paper presents an effective texture descriptor invariant to translation, scaling, and rotation for texture-based image retrieval applications. In order to find the minimal matching distance between two descriptors, existing frequency-layout descriptors require a lot of distance calculations with every possible combination of scaling and rotation values because they are not invariant to geometrical transformation. To cope with this problem, a new compact descriptor is proposed that is theoretically invariant to such transformations. The proposed descriptor is obtained by first calculating the power spectrum of an original texture image for translation invariance and then the power spectrum image is normalized for scale invariance. Finally, modified Zernike moments are calculated for rotation invariance. The proposed algorithm is simpler and lower than conventional algorithms in terms of the computational complexity. The effectiveness of the proposed descriptor for invariant texture retrieval is shown with various texture datasets by comparing the retrieval accuracy, the descriptor size, and the matching complexity of the proposed descriptor with those of conventional descriptors.