Automation and Remote Control

, Volume 77, Issue 3, pp 443–450 | Cite as

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

System Analysis and Operations Research


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.


Remote Control Face Recognition Exhaustive Search Neighbor Search Recognition Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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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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