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Reliability-Based Local Features Aggregation for Image Segmentation

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Advances in Visual Computing (ISVC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10072))

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Abstract

Local features are used for describing the visual information in a local neighborhood of image pixels. Although using various types of local features can provide complementary information about the pixels, effective integration of these features has remained as a challenging issue. In this paper, we propose a novel segmentation algorithm which aggregates the information obtained from different local features. Starting with an over-segmentation of the input image, local features are fed into a factorization-based framework to construct multiple new representations. We then introduce a novel aggregation model to integrate the new representations. Our proposed model jointly learns the reliability of representations and infers final representation. Final segmentation is obtained by applying post-processing steps on the inferred final representation. Experimental results demonstrate the effectiveness of our algorithm on the Berkeley Segmentation Dataset.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, Texas, USA

    Fariba Zohrizadeh, Mohsen Kheirandishfard, Kamran Ghasedidizaji & Farhad Kamangar

Authors
  1. Fariba Zohrizadeh
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  2. Mohsen Kheirandishfard
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  3. Kamran Ghasedidizaji
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  4. Farhad Kamangar
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Corresponding author

Correspondence to Fariba Zohrizadeh .

Editor information

Editors and Affiliations

  1. University of Nevada , Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center , Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory , Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute , Reno, Nevada, USA

    Darko Koracin

  5. The Australian National University , O'Malley, Aust Capital Terr, Australia

    Fatih Porikli

  6. Pilot AI Labs , Redwood City, California, USA

    Sandra Skaff

  7. University of Florida , Gainesville, Florida, USA

    Alireza Entezari

  8. Google Inc. , Mountain View, California, USA

    Jianyuan Min

  9. Osaka University , Osaka, Japan

    Daisuke Iwai

  10. The MOVES Institute , Monterey, California, USA

    Amela Sadagic

  11. University of Arizona , Tucson, Arizona, USA

    Carlos Scheidegger

  12. Université Paris-Sud , Orsay, France

    Tobias Isenberg

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Zohrizadeh, F., Kheirandishfard, M., Ghasedidizaji, K., Kamangar, F. (2016). Reliability-Based Local Features Aggregation for Image Segmentation. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10072. Springer, Cham. https://doi.org/10.1007/978-3-319-50835-1_18

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  • DOI: https://doi.org/10.1007/978-3-319-50835-1_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50834-4

  • Online ISBN: 978-3-319-50835-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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