Automation and Remote Control

, Volume 77, Issue 3, pp 443–450

The maximal likelihood enumeration method for the problem of classifying piecewise regular objects

System Analysis and Operations Research

DOI: 10.1134/S0005117916030061

Cite this article as:
Savchenko, A.V. Autom Remote Control (2016) 77: 443. doi:10.1134/S0005117916030061
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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.

Copyright information

© Pleiades Publishing, Ltd. 2016

Authors and Affiliations

  1. 1.National Research University Higher School of EconomicsLaboratory of Algorithms and Technologies for Network AnalysisNizhny NovgorodRussia

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