Journal of Computer Science

3D Object Recognition using Multiclass Support Vector Machine-K-Nearest Neighbor Supported by Local and Global Feature

R. Muralidharan and C. Chandrasekar

DOI : 10.3844/jcssp.2012.1380.1388

Journal of Computer Science

Volume 8, Issue 8

Pages 1380-1388

Abstract

Problem statement: In this study, a new method has been proposed for the recognition of 3D objects based on the various views of the object. The proposed method is evolved from the two promising methods available for object recognition. Approach: The proposed method uses both the local and global features extracted from the images. For feature extraction, Hu’s Moment invariant is computed for global feature to represent the image and Hessian-Laplace detector and PCA-SIFT descriptor as local feature for the given image. The multi-classs SVM-KNN classifier is applied to the feature vector to recognize the object. The proposed method uses the COIL-100 and CALTECH image databases for its experimentation. Results and Conclusion: The proposed method is implemented in MATLAB and tested. The results of the proposed method are better when comparing with other methods like KNN, SVM and BPN.

Copyright

© 2012 R. Muralidharan and C. Chandrasekar. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.