A Novel Hybrid Model for Image Classification

Abstract:

Image classification poses challenges to retrieval technology. Though the Support Vector Machine (SVM) has been successfully applied to pattern recognition, its performance is limited by the feature space and parameters in the training process. Our work thus has two central themes. Construct the optimum feature space for training SVM from image features extraction by nonlinear dimensionality reduction based on manifold learning, and meanwhile establish the RBF kernel based SVM classifier by training with the best parameters with a global search capacity of the Quantum-behaved Particle Swarm Optimization (QPSO). Experiments show that our model not only improves the learning ability, but also significantly enhances the accuracy of image classification.

• Periodical: Applied Mechanics and Materials (Volumes 48-49)
• Pages: 98-101
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.48-49.98
• Online since: February 2011
• Authors: Jie Xu, Rong Zhu, Bo Hong
• Keywords: Image Classification, Manifold Learning, Quantum-Behaved Particle Swarm Optimization, Support Vector Machine (SVM)

References

[1] del R. Marina, Explicit query formulation with visual keywords, " In Proc. of the 8th ACM Int, l Conf. on Multimedia, 2000, p.407–412.

[2] F. Jing, M. Li, H.J. Zhang and B. Zhang, An efficient and effective region-based image retrieval framework, IEEE Trans. Image Process, 2004, 13(5), p.699–709.

DOI: 10.1109/tip.2004.826125

[3] S. Sadek, A. Al-Hamadi, B. Michaelis and U. Sayed, Robust Image Classification Using Multi-level Neural Networks[C], " IEEE Int, l Conf. on Intelligent Computing and Intelligent Systems, 2009, p.180–183.

DOI: 10.1109/icicisys.2009.5357700

[4] J. Vogel and B. Schiele, Semantic modeling of natural scenes for content-based image retrieval, " Int, l J. of Computer Vision, 2007, 72(2), p.133–157.

DOI: 10.1007/s11263-006-8614-1

[5] S. T. Roweis and L. K. Saul, Nonlinear dimensionality reduction by locally linear embedding [J], Science, 2000, 290(5500), p.2323–2326.

DOI: 10.1126/science.290.5500.2323

[6] V. de Silva and J. B. Tenenbaum, Global versus local methods in nonlinear dimensionality reduction[J], " Neural Information Processing Systems (NIPS, 2002), 2003, p.705–712.

[7] Jun Sun and Wenbo Xu, A Global Search Strategy of Quantum-behaved Particle Swarm Optimization[C], IEEE Conf. on Cybernetics and Intelligent Systems, 2004, p.111–116.

DOI: 10.1109/iccis.2004.1460396

[8] J. B. Tenenbaum, V. Silva and J. C. Langford, A Global geometric framework for nonlinear dimensionality reduction [J], Science, 2000, 290(5500), p.2319–2323.

DOI: 10.1126/science.290.5500.2319

[9] Belkin M and Niyogi P, Laplacian Eigenmaps for Dimensionality Reduction and Data Representation[J], Neural Computation, 2003, 15(6), p.1373–1396.

DOI: 10.1162/089976603321780317