© Elsevier B.V. All rights reserved. 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.
Diaz-Ramirez, V. H., Cuevas, A., Kober, V., Trujillo, L., & Awwal, A. (2015). Pattern recognition with composite correlation filters designed with multi-objective combinatorial optimization. Optics Communications, 77-89. https://doi.org/10.1016/j.optcom.2014.10.038