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Attributed Relational Graph-Based Learning of Object Models for Object Segmentation

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Image Analysis and Recognition (ICIAR 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9164))

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Abstract

In object recognition accurate segmentation of a particular object of interest (OOI) is critical. The OOI usually consists of a set of homogeneous regions with spatial relations among them. Thus, class-specific knowledge on the visual appearance and spatial arrangement of the regions can be useful in discriminating among objects from different classes. In this paper, we propose the use of the Attributed Relational Graph (ARG)-based formalism as a means of representing both visual and spatial information in a single structure. In the proposed framework, a training set of images, each of which contains an instance of the OOI, is given. Afterwards, each image is over-segmented into a set of visually homogeneous regions and the corresponding ARG is constructed. Given such graph representations, OOI model learning reduces to a subgraph matching problem.

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Author information

Authors and Affiliations

  1. Department of Mathematics, Statistics and Computer Science, St. Francis Xavier University, Antigonish, NS, Canada

    Nasreen Akter & Iker Gondra

Authors
  1. Nasreen Akter
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  2. Iker Gondra
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Corresponding author

Correspondence to Iker Gondra .

Editor information

Editors and Affiliations

  1. University of Waterloo, Waterloo, Ontario, Canada

    Mohamed Kamel

  2. University of Porto, Porto, Portugal

    Aurélio Campilho

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Akter, N., Gondra, I. (2015). Attributed Relational Graph-Based Learning of Object Models for Object Segmentation. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2015. Lecture Notes in Computer Science(), vol 9164. Springer, Cham. https://doi.org/10.1007/978-3-319-20801-5_10

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  • DOI: https://doi.org/10.1007/978-3-319-20801-5_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20800-8

  • Online ISBN: 978-3-319-20801-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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