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Journal of Sensors - Volume 2017 2017, Article ID 8513949, 25 pages - https:-doi.org-10.1155-2017-8513949

Research Article

Institute of Medical Equipment, Academy of Military Medical Science, Tianjin 300161, China

The State Key Laboratory of Intelligent Technology and System, Computer Science and Technology School, Tsinghua University, Beijing 100084, China

Correspondence should be addressed to Jinggong Sun

Received 5 June 2016; Revised 17 October 2016; Accepted 19 January 2017; Published 29 March 2017

Academic Editor: Raymond Swartz

Copyright © 2017 Hang Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We propose an optimum pipeline and develop the hybrid representation to produce an effective and efficient visual terrain classification system. The bag of visual words BOVW framework has emerged as a promising approach and effective paradigm for visual terrain classification. The method includes four main steps: 1 feature extraction, 2 codebook generation, 3 feature coding, and 4 pooling and normalization. Recent researches have primarily focused on feature extraction in the development of new handcrafted descriptors that are specific to the visual terrain. However, the effects of other steps on visual terrain classification are still unknown. At the same time, fusion methods are often used to boost classification performance by exploring the complementarity of diverse features. We provide a comprehensive study of all steps in the BOVW framework and different fusion methods for visual terrain classification. Then, multiple approaches in each step and their effects are explored on the visual terrain dataset. Finally, the feature preprocessing technique, improved BOVW framework, and fusion method are used to construct an optimum pipeline for visual terrain classification. The hybrid representation developed by the optimum pipeline performs effectively and rapidly for visual terrain classification in the terrain dataset, outperforming those current methods. Furthermore, it is robust to diverse noises and illumination alterations.

Autor: Hang Wu, Baozhen Liu, Weihua Su, Zihao Chen, Wenchang Zhang, Xudong Ren, and Jinggong Sun

Fuente: https://www.hindawi.com/


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