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International Conference on Image Analysis and Recognition

ICIAR 2016: Image Analysis and Recognition pp 120–130Cite as

Runtime Performance Enhancement of a Superpixel Based Saliency Detection Model

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9730))

Abstract

Reducing computational cost of image processing for various real time computer and robotic vision tasks, e.g. object recognition and tracking, adaptive compression, content aware image resizing, etc. remains a challenge. Saliency detection is often utilized as a pre-processing step for rapid, parallel, bottom-up processing of low level image features to compute saliency map. Subsequent higher level, complex computer vision tasks can then conveniently focus on identified salient locations for further image processing. Thus, saliency detection has successfully mitigated computational complexity of image processing tasks although processing speed enhancement still remains a desired goal. Recent fast and improved superpixel models are furnishing fresh incentive to employ them in saliency detection models to reduce computational complexity and enhance runtime speed. In this paper, we propose use of the superpixel extraction via energy driven sampling (SEEDS) algorithm to achieve processing speed enhancement in an existing saliency detection model. Evaluation results show that our modified model achieves over 60 % processing speed enhancement while maintaining accuracy comparable to the original model.

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Author information

Authors and Affiliations

  1. College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan

    Qazi Aitezaz Ahmed & Mahmood Akhtar

Authors
  1. Qazi Aitezaz Ahmed
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  2. Mahmood Akhtar
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Corresponding author

Correspondence to Qazi Aitezaz Ahmed .

Editor information

Editors and Affiliations

  1. University of Porto, Porto, Portugal

    Aurélio Campilho

  2. Department of Electrical, University of Waterloo, Waterloo, Ontario, Canada

    Fakhri Karray

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Ahmed, Q.A., Akhtar, M. (2016). Runtime Performance Enhancement of a Superpixel Based Saliency Detection Model. In: Campilho, A., Karray, F. (eds) Image Analysis and Recognition. ICIAR 2016. Lecture Notes in Computer Science(), vol 9730. Springer, Cham. https://doi.org/10.1007/978-3-319-41501-7_14

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  • DOI: https://doi.org/10.1007/978-3-319-41501-7_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41500-0

  • Online ISBN: 978-3-319-41501-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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