3D local transform patterns: A new feature descriptor for image retrieval

Abstract

In this paper, authors proposed a novel approach for image retrieval in transform domain using 3D local transform pattern (3D-LTraP). The various existing spatial domain techniques such as local binary pattern (LBP), Local ternary pattern (LTP), Local derivative pattern (LDP) and Local tetra pattern (LTrP) are encoding the spatial relationship between the neighbors with their center pixel in image plane. The first attempt has been made in 3D using spherical symmetric three dimensional local ternary pattern (SS-3D-LTP). But, the performance of SS-3D-LTP is depend on the proper selection of threshold value for ternary pattern calculation. Also, multiscale and color information are missing in SS-3D-LTP method. In the proposed method i.e. 3D-LTraP, the first problem is overcome by using binary approach Similarly, the other lacunas are avoided by using wavelet transform which provide directional as well as multiscale information and color features are embedded in feature generation process itself. Two different databases which included natural and biomedical database (Coral 10 K and OASIS databases) are used for experimental purpose. The experimental results demonstrate a noteworthy improvement in precision and recall as compared to SS-3D-LTP and recent methods. © Springer Science+Business Media Singapore 2017.