Pattern recognition with composite correlation filters designed with multi-objective combinatorial optimization

被引：11

作者：

Diaz-Ramirez, Victor H. ^{[1
]}

Cuevas, Andres ^{[1
]}

Kober, Vitaly ^{[2
]}

Trujillo, Leonardo ^{[3
]}

Awwal, Abdul ^{[4
]}

机构：

[1] Inst Politecn Nacl CITEDI, Tijuana 22510, BC, Mexico

[2] CICESE, Dept Comp Sci, Ensenada 22860, Baja California, Mexico

[3] Inst Tecnol Tijuana, Tijuana 22500, BC, Mexico

[4] Lawrence Livermore Natl Lab, Natl Ignit Facil, Livermore, CA 94551 USA

来源：

OPTICS COMMUNICATIONS | 2015年 / 338卷

关键词：

Object recognition; Composite correlation filters; Multi-objective evolutionary algorithm; Combinatorial optimization; FACE RECOGNITION; TARGET;

D O I：

10.1016/j.optcom.2014.10.038

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

Composite correlation filters are used for solving a wide variety of pattern recognition problems. These filters are given by a combination of several training templates chosen by a designer in an ad hoc manner. In this work, we present a new approach for the design of composite filters based on multi-objective combinatorial optimization. Given a vast search space of training templates, an iterative algorithm is used to synthesize a filter with an optimized performance in terms of several competing criteria. Moreover, by employing a suggested binary-search procedure a filter bank with a minimum number of filters can be constructed, for a prespecified trade-off of performance metrics. Computer simulation results obtained with the proposed method in recognizing geometrically distorted versions of a target in cluttered and noisy scenes are discussed and compared in terms of recognition performance and complexity with existing state-of-the-art filters. (C) 2014 Elsevier B.V. All rights reserved.

引用

页码：77 / 89

页数：13

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