Moment-based local binary patterns: A novel descriptor for invariant pattern recognition applications

作     者：Papakostas, G. A. Koulouriotis, D. E. Karakasis, E. G. Tourassis, V. D. 

作者机构：TEI Kavala Human Machines Interact HMI Lab Dept Ind Informat Kavala 65404 Greece Democritus Univ Thrace Dept Prod Engn & Management GR-67100 Xanthi Greece 

出 版 物：《NEUROCOMPUTING》 (神经计算)

年 卷 期：2013年第99卷

页      面：358-371页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Image moments Moment invariants Local binary patterns Momentgram Feature extraction Computer-robotic vision Pattern recognition 

摘      要：A novel descriptor able to improve the classification capabilities of a typical pattern recognition system is proposed in this paper. The introduced descriptor is derived by incorporating two efficient region descriptors, namely image moments and local binary patterns (LBP), commonly used in pattern recognition applications, in the last decades. The main idea behind this novel feature extraction methodology is the need of improved recognition capabilities, a goal achieved by the combinative use of these descriptors. This collaboration aims to make use of the major advantages each one presents, by simultaneously complementing each other, in order to elevate their weak points. In this way, the useful properties of the moments and moment invariants regarding their robustness to the noise presence, their global information coding mechanism and their invariant behaviour under scaling, translation and rotation conditions, along with the local nature of the LBP, are combined in a single concrete methodology. As a result a novel descriptor invariant to common geometric transformations of the described object, capable to encode its local characteristics, is formed and its classification capabilities are investigated through massive experimental scenarios. The experiments have shown the superiority of the introduced descriptor over the moment invariants, the LBP operator and other well-known from the literature descriptors such as HOG, HOG-LBP and LBP-HF. (C) 2012 Elsevier B.V. All rights reserved.