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Avtomatika i Telemekhanika, 2016, Issue 3, Pages 99–108
(Mi at14404)
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This article is cited in 3 scientific papers (total in 3 papers)
System Analysis and Operations Research
The maximal likelihood enumeration method for the problem of classifying piecewise regular objects
A. V. Savchenko National Research University Higher School of Economics, Laboratory of Algorithms and Technologies for Network Analysis, Nizhny Novgorod, Russia
Abstract:
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.
Citation:
A. V. Savchenko, “The maximal likelihood enumeration method for the problem of classifying piecewise regular objects”, Avtomat. i Telemekh., 2016, no. 3, 99–108; Autom. Remote Control, 77:3 (2016), 443–450
Linking options:
https://www.mathnet.ru/eng/at14404 https://www.mathnet.ru/eng/at/y2016/i3/p99
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Abstract page: | 196 | Full-text PDF : | 63 | References: | 58 | First page: | 39 |
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