Decision-making rule efficiency estimation with applying similarity metrics

• Autorzy: Anokhin M. , Koryttsev I.
• Języki publikacji: EN

Abstrakty

EN

In article the short description of the most often used methods of classification at pattern recognition is given. The main attention is paid to the methods allowing development of a system for image recognition in a real time scale. The features formation method on the base of two-dimensional spatial spectrums of objects images is offered and application of similarity metrics in a decision-making rule for image classification is described. Experimental data of correct and erroneous recognition probabilities as well as image classification time depending on a number of features and on the identification threshold value are presented and analyzed.

Słowa kluczowe

EN

image; classification; probability; recognition of images; Fourier transform; similarity criterion

• Wydawca: Polish Academy of Sciences, Branch in Lublin
• Czasopismo: ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes
• Rocznik: 2015
• Tom: Vol. 4, No 3
• Strony: 73--78
• Opis fizyczny: Bibliogr. 30 poz., rys., wz.

Twórcy

autor

Anokhin M.

• Kharkov National University of Radio Electronics

autor

Koryttsev I.

• Kharkov National University of Radio Electronics

Bibliografia

• 1. Semenets V., Natalukha Yu., Taranukha O., Tokarev V. 2014. About One Method of Mathematical Modelling of Human Vision Functions. ECONTECHMOD. An international quarterly journal Vol. 3, №3, 51-59.
• 2. Jafri R., Arabnia H. 2009. A Survey of Face Recognition Techniques. Journal of Information Processing Systems. 5 (2), 41-68.
• 3. Kanade T. 1973. Picture Processing System by Computer Complex and Recognition of Human Faces. Kyoto University, Japan, PhD Thesis.
• 4. Nixon M. 1985. Eye spacing measurement for facial recognition. SPIE Proceedings. 279-285.
• 5. Reisfeld D. 1994. Generalized symmetry transforms: attentional mechanisms and face recognition. Tel-Aviv University. PhD Thesis.
• 6. Graf H., Chen T., Petajan, E., Cosatto E. 1995. Locating faces and facial parts. International Workshop on Automatic FACE - and Gesture-Recognition. 41-46.
• 7. Cox I., Ghosn J., Yianilos P. 1996. Featurebased face recognition using mixture-distance. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 209-216.
• 8. Lades M., Vorbrüggen J., Buhmann J., LangeJ., Malsburg C., Würtz R., Konen W. 1993. Distortion invariant object recognition in the dynamic link architecture. IEEE Trans. Computers. 42, 300-311.
• 9. Campadelli P., Lanzarotti R. 2005. A Face Recognition System Based on Local Feature Characterization. Advanced Studies in Biometrics. 3161, 147-152.
• 10. Albiol A., Monzo D., Martin A., Sastre J. 2008. Face recognition using HOG–EBGM. Pattern Recognition Letters. 29, 1537-1543.
• 11. Brytik V.I., Zhilina O.Yu., Kobziev V.G. 2014. Structural Method of Describing The Texture Images. ECONTECHMOD. An international quarterly journal Vol. 3, №3, 89-98.
• 12. Baron R. 1981. Mechanisms of Human Facial Recognition. International Journal of Man-Machine Studies. 15, 137-178.
• 13. Sirovich L., Kirby M. 1987. Low-dimensional Procedure for the Characterization of Human Faces. Journal of the Optical Society of America A: Optics, Image Science and Vision. 4, 519-524.
• 14. Turk M., Pentland A. 1991. Face Recognition Using Eigenfaces. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 586-591.
• 15. Moghaddam B., Nastar C., Pentland A. 1996. A Bayesian Similarity Measure for Direct Image Matching. Proceedings 13th International Conference on Pattern Recognition. 350-358.
• 16. DeMers D., Cottrell G. 1993. Non-linear dimensionality reduction. Advances in Neural Information Processing Systems. 5, 580-587.
• 17. Weng J., Ahuja N., Huang T. 1993. Learning recognition and segmentation of 3-D objects from 3-D images. Proceedings of the International Conference on Computer Vision (ICCV 93). 121-128.
• 18. Lawrence S., Giles C., Tsoi A., Back A. 1997. Face Recognition: A Convolutional Neural Network Approach. IEEE Transactions on Neural Networks, Special Issue on Neural Networks and Pattern Recognition. 1-24.
• 19. Turk M., Pentland A. 1991. Eigenfaces For Recognition. Journal Of Cognitive Neuroscience. 3, 71-86.
• 20. Eleyan A., Demirel H. 2005. Face Recognition System Based on PCA and Feedforward Neural Networks. Computational Intelligence and Bioinspired Systems. 3512, 935-942.
• 21. Li B., Yin H. 2005. Face Recognition Using RBF Neural Networks and Wavelet Transform. Advances in Neural Networks. 3497, 105-111.
• 22. Melin P., Felix C., Castillo O. 2005. Face recognition using modular neural networks and the fuzzy Sugeno integral for response integration. International Journal Of Intelligent Systems. 20, 275-291.
• 23. Zhang G., X., Li S., Wang Y., X. 2004. Boosting local binary pattern (LBP)-based face recognition. Advances In Biometric Person Authentication, Proceedings. 3338, 179-186.
• 24. Krebel U. 1999. Pairwise classification and support vector machines. Advance in Kernel Methods – Support Vector Learning. 255-268.
• 25. Burges C. 1998. A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery. 2, 121-267.
• 26. Milborrow S., Morkel J., Nicolls F. 2010. The MUCT Landmarked Face Database. Pattern Recognition Association of South Africa.
• 27. Brigham E. 1988. Fast Fourier Transform and Its Applications. Prentice Hall. 448.
• 28. Gonzales R., Woods R. 2007. Digital Image Processing. Prentice Hall, 976.
• 29. Yaegashi Y., Tateoka K., Fujimoto K., Nakazawa T., Nakata A., Saito Y., Abe T., Yano M, Sakata K. 2012. Assessment of Similarity Measures for Accurate Deformable Image Registration. Journal of Nuclear medicine and Raidation Therapy. 3(4).
• 30. Sung-Huyk C. 2007. Comprehensive Survey on Distance/Similarity Measures between Probability Density Functions. International Journal of Mathematical Models and Methods in Applied Science. 1(4), 300-307.