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

TL;DR: 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.

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 $$92~\% $$ 92 % classification rates for all cases. Moreover, it even achieves $$98~\% $$ 98 % classification rate on the newly built dataset with common light case.