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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