New hierarchical SVM classifier for multi-class target recognition

Yu-Chiang Wang; David Casasent

doi:10.1117/12.637221

24 October 2005 New hierarchical SVM classifier for multi-class target recognition

Yu-Chiang Wang, David Casasent

Proceedings Volume 6006, Intelligent Robots and Computer Vision XXIII: Algorithms, Techniques, and Active Vision; 60060Y (2005) https://doi.org/10.1117/12.637221
Event: Optics East 2005, 2005, Boston, MA, United States

Abstract

We propose a binary hierarchical classifier to solve the multi-class classification problem with aspect variations in objects and with rejection of non-object false targets. The hierarchical architecture design is automated using our new k-means SVRM (support vector representation machine) clustering algorithm. At each node in the hierarchy, we use a new SVRDM (support vector representation and discrimination machine) classifier, which has good generalization and offers good rejection ability. We also provide a theoretical basis for our choice of kernel function (K), and our method of parameter selection (for σ and p). Using this hierarchical SVRDM classifier with magnitude Fourier transform features, experimental results on both simulated and real infra-red (IR) databases are excellent.

Citation Download Citation

Yu-Chiang Wang and David Casasent "New hierarchical SVM classifier for multi-class target recognition", Proc. SPIE 6006, Intelligent Robots and Computer Vision XXIII: Algorithms, Techniques, and Active Vision, 60060Y (24 October 2005); https://doi.org/10.1117/12.637221

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