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Salient Region Detection Using Discriminative Feature Selection

  • Conference paper
Advanced Concepts for Intelligent Vision Systems (ACIVS 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6915))

Abstract

Detecting visually salient regions is useful for applications such as object recognition/segmentation, image compression, and image retrieval. In this paper we propose a novel method based on discriminative feature selection to detect salient regions in natural images. To accomplish this, salient region detection was formulated as a binary labeling problem, where the features that best distinguish a salient region from its surrounding background are empirically evaluated and selected based on a two-class variance ratio. A large image data set was employed to compare the proposed method to six state-of-the-art methods. From the experimental results, it has been confirmed that the proposed method outperforms the six algorithms by achieving higher precision and better F-measurements.

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Author information

Authors and Affiliations

  1. Department of Electronics and Computer Engineering, Hanyang University, Seoul, Republic of Korea

    HyunCheol Kim & Whoi-Yul Kim

Authors
  1. HyunCheol Kim
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  2. Whoi-Yul Kim
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Editor information

Editors and Affiliations

  1. DGA, 7-9 rue des mathurins, 92 221, Bagneux, France

    Jacques Blanc-Talon

  2. VITO-TAP, Boeretang 200, 2400, MOl, Belgium

    Richard Kleihorst

  3. Ghent University, St. -Pietersnieuwstraat 41, B9000, Ghent, Belgium

    Wilfried Philips

  4. CSIRO ICT Centre, Epping,, Po Box 76, 1710, Sydney, NSW, Australia

    Dan Popescu

  5. Physics, University of Antwerp, Universiteitsplein 1; Building N, 2610, Wilrijk, Belgium

    Paul Scheunders

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© 2011 Springer-Verlag Berlin Heidelberg

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Kim, H., Kim, WY. (2011). Salient Region Detection Using Discriminative Feature Selection. In: Blanc-Talon, J., Kleihorst, R., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2011. Lecture Notes in Computer Science, vol 6915. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23687-7_28

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  • DOI: https://doi.org/10.1007/978-3-642-23687-7_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23686-0

  • Online ISBN: 978-3-642-23687-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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