A novel hybrid model for image classification. (14th July 2011)

• Record Type: Journal Article
• Authors: Liu, Yi-Ming; Yao, Min; Zhu, Rong
• Is Part Of: International journal of computational science and engineering. Volume 6:Number 1/2(2011)
• Page Start: 96
• Page End: 104
• Publication Date: 2011-07-14
• Subjects: image classification -- feature selection -- parameter estimation -- support vector machines -- SVM -- quantum-behaved particle swarm optimisation -- QPSO -- PSO -- genetic algorithms; Computer science -- Mathematics -- Periodicals; Computer simulation -- Mathematical aspects -- Periodicals; Computational intelligence -- Periodicals; 004.015105
• Languages: English
• ISSNs: 1742-7185

Abstract

Recently, biological intelligent computing gains more and more attention in analysing large-scale real world datasets. Because the performance of the support vector machine (SVM) classifier is always degraded by poor feature subsets and inappropriate parameters for training, an improved quantum-behaved particle swarm optimisation (IQPSO) is introduced to optimise the features and parameters synchronically, aiming to improve the generalisation of the SVM classifier. That is, a novel hybrid image classification model by combing SVM and IQPSO, called as IQPSO_SVM is presented in this paper. Experimental results show that the proposed IQPSO_SVM improves the classification accuracy greatly compared to the traditional SVM with grid search, and outperforms such SVM based on genetic algorithm (GA_SVM) without accuracy loss.