A convenient feature vector construction for vehicle color recognition

Proc. of the 11th WSEAS Int. Conf. on Neural Networks, NN '10, Proceedings of the 11th WSEAS Int. Conf. on Evolutionary Computing, EC '10, Proc. of the 11th WSEAS Int. Conf. on Fuzzy Systems, FS '10

Volumn , Issue , 2010, Pages 250-255

  Dule, Erida ^a   Gökmen, Muhittin ^a   Beratoǧlu, M Sabur ^a  

^a ISTANBUL TECHNICAL UNIVERSITY   (Turkey)

Author keywords

Classification method; Color spaces; Feature selection; Histogram; Outdoor color; ROI selection; Vehicle color recognition

Indexed keywords

CLASSIFICATION METHODS; COLOR SPACES; FEATURE SELECTION; HISTOGRAM; OUTDOOR COLOR; ROI SELECTION;

FEATURE EXTRACTION; FUZZY SETS; GRAPHIC METHODS; IMAGE ENHANCEMENT; NEURAL NETWORKS; VECTOR SPACES; VECTORS; VEHICLES;

COLOR;

EID: 79952655912     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. N. Beak, S.-M. Park, K.-J. Kim, and S.-B. Park. Vehicle color classification based on the support vector machine method. D.-S. Huang, L. Heutte, and M. Loog(Eds): ICIC 2007, CCIS 2, pp. 1133 - 1139, 2007. Springer-Verlag Berlin Heidelberg, 2007.
2. D. Berwick, and S.W. Lee. A chromaticity space for specularity, illumination color- and illumination pose- invariant 3D object recognition. Proceedings International Conference on Computer Vision, pp. 165-170, 1998.
3. S.D. Buluswar and B.A. Darper. Color Recognition in outdoor images. In IEEE International Conferance on Computer Vision, January 1998.
4. G. Guo, H. Wang and D. Bell. KNN Model-Based Approach in Classification. On The Move to Meaningful Internet Systems, pp. 986-996, Springer Berlin/Heidelberg, October 2003.
5. O. Hasegawa and T. Kanade. Type classification, color estimation, and specific target detection of moving targets on public streets. In Machine Vision and Application, 16: 116-121, 2005. Springer-Verlag, December 2004 (Pubitemid 40536234)
6. T. Joachims, 11 in: Making large-Scale SVM Learning Practical. Advances in Kernel Methods -Support Vector Learning, B. Schölkopf and C. Burges and A. Smola (ed.), MIT Press, 1999.
7. S. Kahraman, O. Gunaydin, M. Alber and M. Fener. 'Evaluating the strength and deformability properties of misis fault breccia using artificial neural networks. Expert Systems with Applications, 36: 6874-6878, 2009.
8. E. Koskivaara. Neural networks in analytical review procedures. Managerial Auditing Journal, 19(2), 191-223. 2004.
9. M. Merler. Car color and logo recognition (Term project presentation), 2006, http://www-cse.ucsd.edu
10. N. Otsu. A threshold selection method from gray-level histograms. IEEE Transitiond on Systems, Man and Cybernetics. Vol 9, pp. 62-66, January 1979. (Pubitemid 9413341)
11. N. Sebe, M.S. Lew. Color-based retrieval. Pattern Recognition Letters, 22:223-230, Elsiever Science B.V., 2001. (Pubitemid 32207841)
12. M. Swain and D. Ballard. Color indexing. International Journal of Computer Vision, 7(11), November 1991.
13. Y. Tsin, R.T. Collins, T. Kanade, and V. Ramesh. Bayesian color constancy for outdoor object recognition. In 2001 IEEE Computer Society Conferance on Computer Vision and Pattern Recognition (CVPR-01)-Volume 1, p. 1132, 2001.
14. W. Wu, Z. QiSen, and W. Mingjun. A method of vehicle classification using models and neural network. In: Proc. IEEE 53rd conference on vehicular technology (VTC2001), 4:3022-3026, 2001.
15. B.D. Zarit, B.J. Super, F.K.H. Quek. Comparison of five color models in skin pixel classification. In: Proc. Int. Workshop on Recognition, Analysis, and Tracking of Faces and Gastures in Real-Time Systems, pp. 58-63, 1999.