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Bayesian genetic programming for edge detection

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Abstract

In edge detection, designing new techniques to combine local features is expected to improve detection performance. However, how to effectively design combination techniques remains an open issue. In this study, an automatic design approach is proposed to combine local edge features using Bayesian programs (models) evolved by genetic programming (GP). Multivariate density is used to estimate prior probabilities for edge points and non-edge points. Bayesian programs evolved by GP are used to construct composite features after estimating the relevant multivariate density. The results show that GP has the ability to effectively evolve Bayesian programs. These evolved programs have higher detection accuracy than the combination of local features by directly using the multivariate density (of these local features) in a simple Bayesian model. From evolved Bayesian programs, the proposed GP system has potential to effectively select features to construct Bayesian programs for performance improvement.

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Authors and Affiliations

  1. School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, New Zealand

    Wenlong Fu & Mengjie Zhang

  2. University of Worcester, Worcester, UK

    Mark Johnston

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  1. Wenlong Fu
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  2. Mengjie Zhang
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  3. Mark Johnston
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Correspondence to Wenlong Fu.

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The author declares that they have no conflict of interest.

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Communicated by V. Loia.

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Fu, W., Zhang, M. & Johnston, M. Bayesian genetic programming for edge detection. Soft Comput 23, 4097–4112 (2019). https://doi.org/10.1007/s00500-018-3059-3

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