Maximum-likelihood approximate nearest neighbor method in real-time image recognition

PATTERN RECOGNITION

卷:61

Article

Savchenko, A. V.¹ 

[1] Natl Res Univ, Higher Sch Econ, Lab Algorithms & Technol Network Anal, 136 Rodionova St, Nizhnii Novgorod 603093, Russia

关键词: Approximate nearest neighbor method;    Large database;    Maximum likelihood;    Real-time pattern recognition;    Image recognition;    Probabilistic neural network;    HOG (histograms of oriented gradients);    Deep neural networks;   

DOI  :  10.1016/j.patcog.2016.08.015

来源: Elsevier

【 摘 要 】

An exhaustive search of all classes in pattern recognition methods cannot be implemented in real-time, if the database contains a large number of classes. In this paper we introduce a novel probabilistic approximate nearest-neighbor (NN) method. Despite the most of known fast approximate NN algorithms, our method is not heuristic. The joint probabilistic densities (likelihoods) of the distances to previously checked reference objects are estimated for each class. The next reference instance is selected from the class with the maximal likelihood. To deal with the quadratic memory requirement of this approach, we propose its modification, which processes the distances from all instances to a small set of pivots chosen with the farthest-first traversal. Experimental study in face recognition with the histograms of oriented gradients and the deep neural network-based image features shows that the proposed method is much faster than the known approximate NN algorithms for medium databases. (C) 2016 Elsevier Ltd. All rights reserved.

【 授权许可】

Free