Open Access

New Proposed Fusion between DCT for Feature Extraction and NSVC for Face Classification


Cite

1. Amine, A., S. Ghouzali, M. Rziza et al. Investigation of Feature Dimension Reduction Based DCT/SVM for Face Recognition. – In: IEEE Symposium on Computers and Communications, 2008 (ISCC’2008), IEEE, 2008, pp. 188-193.10.1109/ISCC.2008.4625662Search in Google Scholar

2. Chen, P.-Y., C.-C. Huang, C.-Y. Lien et al. An Efficient Hardware Implementation of HOG Feature Extraction for Human Detection. – IEEE Transactions on Intelligent Transportation Systems, Vol. 15, 2014, No 2, pp. 656-662.10.1109/TITS.2013.2284666Search in Google Scholar

3. Teoh, S. S., T. Braunl. Performance Evaluation of HOG and Gabor Features for Vision-Based Vehicle Detection. – In: IEEE International Conference on Control System, Computing and Engineering (ICCSCE’15), IEEE 2015, pp. 66-71.10.1109/ICCSCE.2015.7482159Search in Google Scholar

4. Ngadi, M., A. Amine, H. Hachimi, A. El-Attar. A New Optimal Approach for Breast Cancer Diagnosis Classification. – International Journal of Imaging and Robotics, Vol. 16, 2016, Issue No 4, pp. 25-36.Search in Google Scholar

5. Gupta, I., S. Kaur, P. Sahni et al. Novel Human Age Estimation System Based on DCT Features and Locality-Ordinal Information. – In: International Conference on. Inventive Computation Technologies (ICICT’16), 2016, IEEE, pp. 1-4.10.1109/INVENTIVE.2016.7823181Search in Google Scholar

6. Yang, X., A. Cao, Q. Song, G. Schaefer, Y. Su. Vicinal Support Vector Classifier Using Supervised Kernel-Based Clustering. – Artificial Intelligence in Medicine, 2014.10.1016/j.artmed.2014.01.003Search in Google Scholar

7. Cao, A., Q. Song, X. Yang, S. Liu, C. Guo. Mammographic Mass Detection by Vicinal Support Vector Machine. – In: IEEE International Joint Conference on Neural Networks, IEEE, Budapest, Hungary, Vol. 3, 2004, pp. 1953-1958.Search in Google Scholar

8. Vapnik, V. The Nature of Statistical Learning Theory. 2nd Edition. NY, USA, Springer Verlag, 2000.10.1007/978-1-4757-3264-1Search in Google Scholar

9. Chapelle, O., J. Weston, L. Bottou, V. Vapnik. Vicinal Risk Minimization. – Advances in Neural Information Processing Systems, Vol. 13, MIT Press, MA, USA, 2000, pp. 416-422.Search in Google Scholar

10. Camastra, F., A. Verri. A Novel Kernel Method for Clustering. – IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, 2005, No 5, pp. 801-805.10.1109/TPAMI.2005.88Search in Google Scholar

11. Leski, J. Fuzzy c-Varieties/Elliptotypes Clustering in Reproducing Kernel Hilbert Space. – Fuzzy Sets and Systems, Vol. 141, 2004, No 2, pp. 259-280.10.1016/S0165-0114(03)00184-2Search in Google Scholar

12. Rose, K. Deterministic Annealing for Clustering, Compression, Classification, Regression, and Related Optimization Problems. – Proceedings of the IEEE, 1998, pp. 2210-2239.10.1109/5.726788Search in Google Scholar

13. Zheng, B., S. W. Yoon, S. S. Lam. Breast Cancer Diagnosis Based on Feature Extraction Using a Hybrid of k-Means and Support Vector Machine Algorithms. – Expert Systems with Applications, Vol. 41, 2014, No 4, pp. 1476-1482.10.1016/j.eswa.2013.08.044Search in Google Scholar

eISSN:
1314-4081
Language:
English
Publication timeframe:
4 times per year
Journal Subjects:
Computer Sciences, Information Technology