The maximal likelihood enumeration method for the problem of classifying piecewise regular objects
- First Online:
- 26 Downloads
We study the recognition problem for composite objects based on a probabilistic model of a piecewise regular object with thousands of alternative classes. Using the model’s asymptotic properties, we develop a new maximal likelihood enumeration method which is optimal (in the sense of choosing the most likely reference for testing on every step) in the class of “greedy” algorithms of approximate nearest neighbor search. We show experimental results for the face recognition problem on the FERET dataset. We demonstrate that the proposed approach lets us reduce decision making time by several times not only compared to exhaustive search but also compared to known approximate nearest neighbors techniques.
Unable to display preview. Download preview PDF.
- 1.Pattern Recognition, Theodoridis, S. and Koutroumbas, K., Eds., Boston: Academic, 2008.Google Scholar
- 3.Abusev, R.A. and Lumel’skii, Ya.P., Statistical Models for Classifying Multidimensional Observations, Obozren. Prikl. Promyshl. Mat., 1996, vol. 3, no. 1, pp. 7–30.Google Scholar
- 4.Springer Handbook of Speech Recognition, Benesty, J., Sondh, M., and Huang, Y., Eds., New York: Springer, 2008.Google Scholar
- 5.Savchenko, A.V., Image as a Collection of Samples of Independent Identically Distributed Values of Features in Recognition Problems for Objects with Complex Structure, Zav. Lab. Diagnostika Materialov, 2014, vol. 80, no. 3, pp. 70–80.Google Scholar
- 6.Dalal, N. and Triggs, B., Histograms of Oriented Gradients for Human Detection, Proc. IEEE Conf. on Computer Vision & Pattern Recognition, San Diego, 2005, pp. 886–893.Google Scholar
- 8.Silpa-Anan C. and Hartley, R., Optimised KD-trees for Fast Image Descriptor Matching, Proc. IEEE Conf. on Computer Vision & Pattern Recognition, Alaska, 2008, pp. 1–8.Google Scholar
- 10.Borovkov, A.A., Matematicheskaya statistika. Dopolnitel’nye glavy (Mathematical Statistics. Additional Chapters), Moscow: Nauka, 1984.Google Scholar
- 15.Chen, C.C., Hierarchical Particle Swarm Optimization for Optimization Problems, Tamkang J. Sci. Eng., 2009, vol. 12, no. 3, pp. 289–298.Google Scholar