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A Similarity-Based Approach for Shape Classification Using Region Decomposition

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Book cover Intelligent Computing Theories and Application (ICIC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9772))

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Abstract

Measuring the similarity of two shapes is an important task in human vision systems in order to either recognize or classify the objects. For obtaining reliable results, a high discriminative shape descriptor should be extracted by considering both global and local information of the shape. Taking into account, this work introduces a centroid-based tree-structured (CENTREES) shape descriptor invariant to rotation and scale. Extracting the CENTREES descriptor is started by computing the central of mass of a binary shape, assigned as the root node of tree. The entire shape is then decomposed into b sub-shapes by voting each pixel point according to an angle between point and major principal axis relative to a centroid. In the same way, the central of mass of the sub-shapes are calculated and these locations are considered as level-1 nodes. These processes are repeated for a predetermined number of levels. For each node corresponding to sub-shapes, parameters invariant to translation, rotation and scale are extracted. A vector of all parameters is considered as descriptor. A feature-based template matching with X 2 distance function is used to measure shape dissimilarity. The evaluation of our descriptor is conducted using MPEG-7 dataset. The results justify that the CENTREES is one of reliable shape descriptors for shape similarity.

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Authors and Affiliations

  1. Intelligent Systems Laboratory, Graduate School of Electrical Engineering, University of Ulsan, Ulsan, 680-749, Korea

    Wahyono, Laksono Kurnianggoro, Yu Yang & Kang-Hyun Jo

Authors
  1. Wahyono
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  2. Laksono Kurnianggoro
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  3. Yu Yang
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  4. Kang-Hyun Jo
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Corresponding author

Correspondence to Kang-Hyun Jo .

Editor information

Editors and Affiliations

  1. Tongji University , Shanghai, China

    De-Shuang Huang

  2. University of Ulsan , Ulsan, Korea (Republic of)

    Kang-Hyun Jo

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Wahyono, Kurnianggoro, L., Yang, Y., Jo, KH. (2016). A Similarity-Based Approach for Shape Classification Using Region Decomposition. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9772. Springer, Cham. https://doi.org/10.1007/978-3-319-42294-7_24

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  • DOI: https://doi.org/10.1007/978-3-319-42294-7_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42293-0

  • Online ISBN: 978-3-319-42294-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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