Object Recognition using SVM-KNN based on Geometric Moment Invariant
 | International Journal of Computer Trends and Technology (IJCTT) |  |
| © July to Aug Issue 2011 by IJCTT Journal | ||
| Volume-1 Issue-3 | ||
| Year of Publication : 2011 | ||
| Authors : R.Muralidharan, Dr.C.Chandrasekar. |
R.Muralidharan, Dr.C.Chandrasekar. "Object Recognition using SVM-KNN based on Geometric Moment Invariant"International Journal of Computer Trends and Technology (IJCTT),V1(3):296-301 July to Aug Issue 2011 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract: ——In this paper, a framework for recognizing an object from the given image is discussed. The proposed method is fusion of two popular methods in the literature, K-Nearest Neighbor (KNN) and Support Vector Machine (SVM). We propose the use of KNN to find closest neighbors to a query image and train a local SVM that preserves the distance function on the collection of neighbors. The proposed method is implemented in two steps. The first one concerns KNN to compute distances of the query to all training and pick the nearest K neighbors. The second step is to recognize the object using SVM classifier. For feature vector formation, Hu’s Moment Invariant is computed to represent the image, which is invariant to translation, rotation and scaling. Experimental results are shown for COIL-100 database. Comparative analysis of proposed method with SVM and KNN is also given for each experiment
References-
[1] Ahmed Fawzi Otoom, Hatice Gunes, Massimo Piccardi, “Feature extraction techniques for abandoned object classification in video surveillance”, IEEE Proceedings of the International Conference on Image processing, pp 1368 – 1371, 2008.
[2] Borji, A.; Hamidi, M., Support Vector Machine for Persian Font Recognition, World Academy of Science, Engineering and Technology, 28, 2007, pp.10 – 13.
[3] Chun-Jung Hsu; Chi-Farn Chen; Chau Lee; Shu-Meng Huang. Airport Pavement Distress Image Classification Using Moment Invariant Neural Network, CRISP, 2001, pp. 123 – 127.
[4] Daniel Madan Raja, S.; Shanmugam, A. Artificial Neural Network Based War Scene Classification using Invariant Moments and GLCM Features : A Comparative Study, International Journal of Engineering Science and Technology, 3, 2, 2011, pp. 1189 – 1195.
[5] S. Dudani, K. Breeding, R. McGhee, “Aircraft Identification by moment Invariants,” IEEE Transactions on Computers 26(1), 1977, pp. 39 – 45.
[6] J. Gao, Z. Xie, and X. Wu, “Generic object recognition with regional statistical models and layer joint boosting,” Pattern Recognition Letters, vol. 28, no. 16, pp. 2227-2237, 2007.
[7] Haykin S, Neural Network: A Comprehensive Foundation, Prentice Hall, New Jersey, 1999.
[8] Hu, M.K,: Visual Problem recognition by Moment Invariant, IRE Trans. Inform. Theory, IT-8, 1962, pp.179-187.
[9] Krishna Singh; Indra Gupta; Sangeeta Gupa. SVM-BDT PNN and Fourier Moment Technique for Classification of Leaf Shape, International Journal of Signal Processing, Image Processing and Pattern Recognition, 3, 4, 2010, pp. 67-78.
[10] LI LiLi, ZHANG YanXia and ZHAO YongHeng, “K-Nearest Neighbors for automated classification of celestial objects,” Science in China Series G-Phys Mech Astron, Vol.51, no.7, July 2008, pp. 916- 922.
[11] Luo Juan and Oubong Gwun, “A Comparison of SIFT, PCA-SFIT and SURF,” International Journal of Image Processing, Vol.3. Issue.4. 2009, pp. 143 – 142,.
[12] Muralidharan, R.; Chandrasekar, C. Scale Invariant Feature Extraction for Indentifying an object in the image using Moment Invariants, Proceedings of IEEE International Conference on Communication and Computational Intelligence, 2010, pp.442 – 448.
[13] Nene S.A; Nayar S.K; Murase H, Columbia object image library (coil- 100). Technical report, Colmubia University, 1996.
[14] Rajasekaran, S.; Vijayalakshmi Pai, G.A. Image recognition using Simplified Fuzzy ARTMAP augmented with a moment based feature extractor, International Journal of Pattern Recognition and Artifical Intelligence, 14 – 8, 2000 pp. 1081-1095.
[15] Ronald A. Skoog; Thomas C. Banwell; Joel W. Gannett; Sarry F. Habbiby; Marcus Pang; Michael E. Rauch; Paul Toliver. Automatic Identification of Impairments Using Support Vector Machine Pattern Classification on Eye Diagrams, IEEE Photonics Technology Letters, 18, 22, 2006, pp. 2398 – 2400.
[16] Xin-Han Huang; Xiang-Jin Zeng; Min wang, SVM-Based Identification and Un-calibrated Visual Servoing for Micro-Manipulation, International Journal of Automation and Computing, 7, 1, 2010, pp.47-
Keywords-Support Vector Machine, Moment Invariant, K nearest neighbor, Object Recognition.