A study of Bessel Fourier moments invariant for image retrieval and classification

Various types of orthogonal moments have been widely used for object recognition and classification. This paper presents an effective way of extracting texture features, Bessel Fourier moments, for image retrieval and classification applications. The Bessel Fourier moments are calculated for rotation invariance and perform better in terms of represent global features than orthogonal Fourier-Mellin and Zernike moments. In order to achieve good results in CBIR experiments and image classification experiments using Bessel Fourier moments, we conduct experiments on four databases: the first one is a small 2D texture database formed by 400 images, and the rest are 2D color image databases with different relevant images formed by 1200 from the Amsterdam Library of Object Image (ALOI). The experiments show that the feature descriptors extracting with the proposed algorithm perform better for image retrieval and classification than conventional descriptors by comparing the retrieval accuracy, the same order.