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

Abstract

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.

  1. Victor H Diaz-Ramirez, Andres Cuevas, Vitaly Kober, Leonardo Trujillo and Abdul Awwal. Pattern recognition with composite correlation filters designed with multi-objective combinatorial optimization. Optics Communications 338:77–89, 2015. URL, DOI BibTeX

    @article{Diaz-Ramirez2015,
    	author = "Diaz-Ramirez, Victor H. and Cuevas, Andres and Kober, Vitaly and Trujillo, Leonardo and Awwal, Abdul",
    	doi = "10.1016/j.optcom.2014.10.038",
    	file = ":home/emigdio/Documents/Mendeley Desktop/2015/Diaz-Ramirez et al. - 2015 - Pattern recognition with composite correlation filters designed with multi-objective combinatorial optimiza.pdf:pdf",
    	issn = 00304018,
    	journal = "Optics Communications",
    	keywords = "Combinatorial optimization,Composite correlation filters,Multi-objective evolutionary algorithm,Object recognition",
    	pages = "77--89",
    	publisher = "Elsevier",
    	title = "{Pattern recognition with composite correlation filters designed with multi-objective combinatorial optimization}",
    	url = "http://linkinghub.elsevier.com/retrieve/pii/S0030401814009547",
    	volume = 338,
    	year = 2015
    }
    

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