Retrieval and classification of objects using generic Fourier, Legendre moment, and wavelet Zernike moment descriptors and recognition using joint transform correlator

Abstract The retrieval and classification of shape-based objects employing three descriptors—generic Fourier descriptor (GFD), Legendre moment descriptor (LMD), and wavelet Zernike moment descriptor (WZMD) are described. All three descriptors have been applied for shape retrieval to a database of 1000 shapes from 20 different classes, where each class consists of 50 shapes. The Euclidean distance has been calculated as a similarity measure parameter for shape classification. To study the effect of noise on the retrieval and classification, additive and multiplicative noise of various variances were applied to the database. The classification results have been compared and it is inferred that WZMD performs better than GFD and LMD techniques. Precision and recall were measured as parameters of performance metric. For retrieved shape recognition, an optical experiment employing joint transform correlator architecture has been carried out.