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Interval type-2 fuzzy kernel based support vector machine algorithm for scene classification of humanoid robot

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Abstract

This paper proposed an Interval Type-2 Fuzzy Kernel based Support Vector Machine (IT2FK-SVM) for scene classification of humanoid robot. Type-2 fuzzy sets have been shown to be a more promising method to manifest the uncertainties. Kernel design is a key component for many kernel-based methods. By integrating the kernel design with type-2 fuzzy sets, a systematic design methodology of IT2FK-SVM classification for scene images is presented to improve robustness and selectivity in the humanoid robot vision, which involves feature extraction, dimensionality reduction and classifier learning. Firstly, scene images are represented as high dimensional vector extracted from intensity, edge and orientation feature maps by biological-vision feature extraction method. Furthermore, a novel three-domain Fuzzy Kernel-based Principal Component Analysis (3DFK-PCA) method is proposed to select the prominent variables from the high-dimensional scene image representation. Finally, an IT2FM SVM classifier is developed for the comprehensive learning of scene images in complex environment. Different noisy, different view angle, and variations in lighting condition can be taken as the uncertainties in scene images. Compare to the traditional SVM classifier with RBF kernel, MLP kernel, and the Weighted Kernel (WK), respectively, the proposed method performs much better than conventional WK method due to its integration of IT2FK, and WK method performs better than the single kernel methods (SVM classifier with RBF kernel or MLP kernel). IT2FK-SVM is able to deal with uncertainties when scene images are corrupted by various noises and captured by different view angles. The proposed IT2FK-SVM method yields over \(92~\% \) classification rates for all cases. Moreover, it even achieves \(98~\% \) classification rate on the newly built dataset with common light case.

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Acknowledgments

The authors would like to appreciate the associated editors and the reviewers for the constructive comments and suggestions. This work was supported by the National Natural Science Foundation of China under Project 60974047 and U1134004, Natural Science Foundation of Guangdong Province S2012010008967 and Science Fund for Distinguished Young Scholars (S20120011437), 2011Zhujiang New Star, FOK Ying Tung Education Foundation of China 121061, the Ministry of education of New Century Excellent Talent, the 973 Program of China 2011CB013104, and by the Doctoral Fund of Ministry of Education of China under Grant 20124420130001.

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Authors and Affiliations

  1. School of Automation, Guangdong University of Technology, Guangzhou, China

    Zhi Liu, Shuqiong Xu & Yun Zhang

  2. School of Mechanical Engineering, Guangdong University of Technology, Guangzhou, China

    Xin Chen

  3. Faculty of Science and Technology, University of Macau, Av. Padre Tomás Pereira, S.J, Taipa, Macau, S.A.R, People’s Republic of China

    C.L. Philip Chen

  4. Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX, 78249-0669, USA

    C.L. Philip Chen

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  1. Zhi Liu
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Correspondence to Zhi Liu.

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Communicated by H. Hagras.

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Liu, Z., Xu, S., Zhang, Y. et al. Interval type-2 fuzzy kernel based support vector machine algorithm for scene classification of humanoid robot. Soft Comput 18, 589–606 (2014). https://doi.org/10.1007/s00500-013-1080-0

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