TY - JOUR
T1 - Pattern recognition with composite correlation filters designed with multi-objective combinatorial optimization
AU - Diaz-Ramirez, Victor H.
AU - Cuevas, Andres
AU - Kober, Vitaly
AU - Trujillo, Leonardo
AU - Awwal, Abdul
N1 - Publisher Copyright:
© Elsevier B.V. All rights reserved.
PY - 2015/3/1
Y1 - 2015/3/1
N2 - 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.
AB - 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.
KW - Combinatorial optimization
KW - Composite correlation filters
KW - Multi-objective evolutionary algorithm
KW - Object recognition
UR - http://www.scopus.com/inward/record.url?scp=84908544514&partnerID=8YFLogxK
U2 - 10.1016/j.optcom.2014.10.038
DO - 10.1016/j.optcom.2014.10.038
M3 - Artículo
SN - 0030-4018
VL - 338
SP - 77
EP - 89
JO - Optics Communications
JF - Optics Communications
ER -