Effective T2FPSO-Based T2FSVM Scene Classification Algorithm

doi:10.16182/j.issn1004731x.joss.201612007

Abstract

Abstract: A systematic design methodology of Type-2 Fuzzy Particle Swarm Optimization (T2FPSO) based Type-2 Fuzzy Support Vector Machine (T2FSVM) classification system was proposed for scene image to improve selectivity and robustness in the machine vision. In the novel classification system, the T2FSVM model was presented to realize a comprehensive learning of the correct class and show the superiority of the generalization capability for classification problem. Furthermore, in order to improve the performance of PSO on complex uncertain environments, the type-2 fuzzy concept was incorporated to PSO to construct T2FPSO searching algorithm, in which the interval type-2 fuzzy inertia weight was designed using an Interval Type-2 Fuzzy Logic System (IT2FLS). Experimental studies indicate that the T2FPSO-T2FSVM approach is effective to deal with uncertainties for scene classification, when scene images are corrupted by the hybrid noises or captured by different view angels and light conditions.

Key words: scene classification, type-2 fuzzy particle swarm optimization (T2FPSO), type-2 fuzzy support vector machine (T2FSVM), type-2 fuzzy logic system (T2FLS)

CLC Number:

TP391.9

Xu Shuqiong, Yuan Conggui. Effective T2FPSO-Based T2FSVM Scene Classification Algorithm[J]. Journal of System Simulation, 2016, 28(12): 2925-2933.

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