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Evolutionary Image Analysis and Signal Processing pp 73–90Cite as

Genetic Programming for Generative Learning and Recognition of Hand-Drawn Shapes

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 213))

Abstract

We propose a novel method of evolutionary visual learning that uses a generative approach to assess the learner’s ability to recognize image contents. Each learner, implemented as a genetic programming (GP) individual, processes visual primitives that represent local salient features derived from the input image. The learner analyzes the visual primitives, which involves mostly their grouping and selection, eventually producing a hierarchy of visual primitives build upon the input image. Based on that it provides partial reproduction of the shapes of the analyzed objects and is evaluated according to the quality of that reproduction.We present the method in detail and verify it experimentally on the real-world task of recognition of hand-drawn shapes. In particular, we show how GP individuals trained on examples from different decision classes can be combined to build a complete multiclass recognition system. We compare such recognition systems to reference methods, showing that our generative learning approach provides similar results. This chapter also contains detailed analysis of processing carried out by an exemplary individual.

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Authors and Affiliations

  1. Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60965, Poznań, Poland

    Wojciech Jaśkowski, Krzysztof Krawiec & Bartosz Wieloch

Authors
  1. Wojciech Jaśkowski
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  2. Krzysztof Krawiec
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  3. Bartosz Wieloch
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Editors and Affiliations

  1. Dipartimnto di Ingegneria dell’Informazione , Viale Usberti 181a, 43100, PARMA, Italy

    Stefano Cagnoni

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Jaśkowski, W., Krawiec, K., Wieloch, B. (2009). Genetic Programming for Generative Learning and Recognition of Hand-Drawn Shapes. In: Cagnoni, S. (eds) Evolutionary Image Analysis and Signal Processing. Studies in Computational Intelligence, vol 213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01636-3_5

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  • DOI: https://doi.org/10.1007/978-3-642-01636-3_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01635-6

  • Online ISBN: 978-3-642-01636-3

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