Research Article Open Access

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

R. Muralidharan1 and C. Chandrasekar2
  • 1 KSR College of Engineering, India
  • 2 Periyar University, India
Journal of Computer Science
Volume 8 No. 8, 2012, 1380-1388

DOI: https://doi.org/10.3844/jcssp.2012.1380.1388

Submitted On: 9 May 2012 Published On: 4 August 2012

How to Cite: Muralidharan, R. & Chandrasekar, C. (2012). 3D Object Recognition using Multiclass Support Vector Machine-K-Nearest Neighbor Supported by Local and Global Feature. Journal of Computer Science, 8(8), 1380-1388. https://doi.org/10.3844/jcssp.2012.1380.1388

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.

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Keywords

  • Support vector machine
  • moment invariant
  • hessian-Laplace
  • k nearest neighbor
  • object recognition