Unsupervised Image Segmentation Using a Hierarchical Clustering Selection Process (original) (raw)

Abstract

In this paper we present an unsupervised algorithm to select the most adequate grouping of regions in an image using a hierarchical clustering scheme. Then, we introduce an optimisation approach for the whole process. The grouping method presented is based on the maximisation of a measure that represents the perceptual decision. The whole strategy takes profit from a hierarchical clustering to find a maximum of the proposed criterion. The algorithm has been used to segment real images as well as multispectral images achieving very accurate results on this task.

This work has been partly supported by projects ESP2005-07724-C05-05 from Spanish CICYT and P1-1B2004-36 from Fundació Caixa-Castelló.

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Authors and Affiliations

  1. Dept. Lenguajes y Sistemas Informáticos, Jaume I Univerisity,
    Adolfo Martínez-Usó, Filiberto Pla & Pedro García-Sevilla

Authors

  1. Adolfo Martínez-Usó
  2. Filiberto Pla
  3. Pedro García-Sevilla

Editor information

Editors and Affiliations

  1. Hong Kong University of Science and Technology,
    Dit-Yan Yeung
  2. Department of Computer Science, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
    James T. Kwok
  3. Instituto de Telecomunicações, Instituto Superior Técnico, Lisbon, Portugal
    Ana Fred
  4. Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi, 09123, Cagliari, Italy
    Fabio Roli
  5. Faculty of Electrical Engineering, Mathematics and Computer Science, Information and Communication Theory Group, Delft University of Technology, Delft, The Netherlands
    Dick de Ridder

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© 2006 Springer-Verlag Berlin Heidelberg

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Martínez-Usó, A., Pla, F., García-Sevilla, P. (2006). Unsupervised Image Segmentation Using a Hierarchical Clustering Selection Process. In: Yeung, DY., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2006. Lecture Notes in Computer Science, vol 4109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11815921\_88

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