Local and global features based image retrieval system using orthogonal radial moments

Orthogonal radial moments such as Zernike moments, pseudo Zernike moments, and orthogonal Fourier Mellin moments have been studied extensively in the literature. In conventional methods of moment computation, the entire image intensity function is projected upon orthogonal moment polynomials to compute the global features. In this paper, we provide a novel methodology for the computation of these moments, which formulate them as effective local descriptors. The moment functions of edge images are computed to determine the local change in images, which provide the local aspects of the image. Thus, the obtained local and global moment features are combined to evaluate their performance on the image retrieval system. The moments ZMs, PZMs, and OFMMs, are compared in terms of their image retrieval effectiveness. The experiments are performed on various databases to examine the performance of the system in diverse circumstances such as images affected with noise, partial occlusion, distortion, complex structure, etc. The experiments results reveal that the proposed system outperforms the existing recent approaches to moments computation for image retrieval.