Interest point detection through multiobjective genetic programming

Published in Journal Articles

Abstract

The detection of stable and informative image points is one of the most important low-level problems in modern computer vision. This paper proposes a multiobjective genetic programming (MO-GP) approach for the automatic synthesis of operators that detect interest points. The proposal is unique for interest point detection because it poses a MO formulation of the point detection problem. The search objectives for the MO-GP search consider three properties that are widely expressed as desirable for an interest point detector, these are: (1) stability; (2) point dispersion; and (3) high information content. The results suggest that the point detection task is a MO problem, and that different operators can provide different trade-offs among the objectives. In fact, MO-GP is able to find several sets of Pareto optimal operators, whose performance is validated on standardized procedures including an extensive test with 500 images; as a result, we could say that all solutions found by the system dominate previously man-made detectors in the Pareto sense. In conclusion, the MO formulation of the interest point detection problem provides the appropriate framework for the automatic design of image operators that achieve interesting trade-offs between relevant performance criteria that are meaningful for a variety of vision tasks.

  1. Gustavo Olague and Leonardo Trujillo. Interest point detection through multiobjective genetic programming. Appl. Soft Comput. 12(8):2566-2582, 2012. BibTeX

    @article{DBLP:journals/asc/OlagueT12,
    	author = "Gustavo Olague and Leonardo Trujillo",
    	title = "Interest point detection through multiobjective genetic programming",
    	journal = "Appl. Soft Comput.",
    	volume = 12,
    	number = 8,
    	year = 2012,
    	pages = "2566-2582",
    	ee = "http://dx.doi.org/10.1016/j.asoc.2012.03.058",
    	bibsource = "DBLP, http://dblp.uni-trier.de"
    }
    

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Evolutionary-computer-assisted design of image operators that detect interest points using genetic programming

Published in Journal Articles

Abstract

This work describes a way of designing interest point detectors using an evolutionary-computer-assisted design approach. Nowadays, feature extraction is performed through the paradigm of interest point detection due to its simplicity and robustness for practical applications such as: image matching and view-based object recognition. Genetic programming is used as the core functionality of the proposed human-computer framework that significantly augments the scope of interest point design through a computer assisted learning process. Indeed, genetic programming has produced numerous interest point operators, many with unique or unorthodox designs. The analysis of those best detectors gives us an advantage to achieve a new level of creative design that improves the perspective for human-machine innovation. In particular, we present two novel interest point detectors produced through the analysis of multiple solutions that were obtained through single and multi-objective searches. Experimental results using a well-known testbed are provided to illustrate the performance of the operators and hence the effectiveness of the proposal.

  1. Gustavo Olague and Leonardo Trujillo. Evolutionary-computer-assisted design of image operators that detect interest points using genetic programming. Image Vision Comput. 29(7):484-498, 2011. BibTeX

    @article{DBLP:journals/ivc/OlagueT11,
    	author = "Gustavo Olague and Leonardo Trujillo",
    	title = "Evolutionary-computer-assisted design of image operators that detect interest points using genetic programming",
    	journal = "Image Vision Comput.",
    	volume = 29,
    	number = 7,
    	year = 2011,
    	pages = "484-498",
    	ee = "http://dx.doi.org/10.1016/j.imavis.2011.03.004",
    	bibsource = "DBLP, http://dblp.uni-trier.de"
    }
    

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Automated design of image operators that detect interest points

Published in Journal Articles

Abstract

This work describes how evolutionary computation can be used to synthesize low-level image operators that detect interesting points on digital images. Interest point detection is an essential part of many modern computer vision systems that solve tasks such as object recognition, stereo correspondence, and image indexing, to name but a few. The design of the specialized operators is posed as an optimization/search problem that is solved with genetic programming (GP), a strategy still mostly unexplored by the computer vision community. The proposed approach automatically synthesizes operators that are competitive with state-of-the-art designs, taking into account an operator's geometric stability and the global separability of detected points during fitness evaluation. The GP search space is defined using simple primitive operations that are commonly found in point detectors proposed by the vision community. The experiments described in this paper extend previous results (Trujillo and Olague, 2006a,b) by presenting 15 new operators that were synthesized through the GP-based search. Some of the synthesized operators can be regarded as improved manmade designs because they employ well-known image processing techniques and achieve highly competitive performance. On the other hand, since the GP search also generates what can be considered as unconventional operators for point detection, these results provide a new perspective to feature extraction research.

  1. Leonardo Trujillo and Gustavo Olague. Automated design of image operators that detect interest points. Evol. Comput. 16(4):483–507, 2008. URL, DOI BibTeX

    @article{Trujillo:2008:ADI:1479056.1479060,
    	author = "Trujillo, Leonardo and Olague, Gustavo",
    	title = "Automated design of image operators that detect interest points",
    	journal = "Evol. Comput.",
    	issue_date = "Winter 2008",
    	volume = 16,
    	number = 4,
    	month = "",
    	year = 2008,
    	issn = "1063-6560",
    	pages = "483--507",
    	numpages = 25,
    	url = "http://dx.doi.org/10.1162/evco.2008.16.4.483",
    	doi = "10.1162/evco.2008.16.4.483",
    	acmid = 1479060,
    	publisher = "MIT Press",
    	address = "Cambridge, MA, USA",
    	keywords = "Feature detection, computer vision, genetic programming, interest points"
    }
    

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