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FASA: Fast, Accurate, and Size-Aware Salient Object Detection

  • Gökhan YildirimEmail author
  • Sabine Süsstrunk
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9005)

Abstract

Fast and accurate salient-object detectors are important for various image processing and computer vision applications, such as adaptive compression and object segmentation. It is also desirable to have a detector that is aware of the position and the size of the salient objects. In this paper, we propose a salient-object detection method that is fast, accurate, and size-aware. For efficient computation, we quantize the image colors and estimate the spatial positions and sizes of the quantized colors. We then feed these values into a statistical model to obtain a probability of saliency. In order to estimate the final saliency, this probability is combined with a global color contrast measure. We test our method on two public datasets and show that our method significantly outperforms the fast state-of-the-art methods. In addition, it has comparable performance and is an order of magnitude faster than the accurate state-of-the-art methods. We exhibit the potential of our algorithm by processing a high-definition video in real time.

Keywords

Color Histogram Salient Object Salient Region Saliency Detection Visual Saliency 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This work was supported by the Swiss National Science Foundation under grant number 200021-143406/1.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Computer and Communication SciencesÉcole Polytechnique Fédérale de LausanneLausanneSwitzerland

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