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Salient Object Subitizing

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Visual Saliency: From Pixel-Level to Object-Level Analysis

Abstract

As early as the nineteenth century, it was observed that humans can effortlessly identify the number of items in the range of 1–4 by a glance.

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Notes

  1. 1.

    http://www.cs.bu.edu/groups/ivc/Subitizing/.

  2. 2.

    We use the subset of ImageNet images with bounding box annotations.

  3. 3.

    The F-score is computed as \(\frac {2RP}{(R+P)}\), where R and P denote recall and precision respectively.

  4. 4.

    When evaluated on the test set used by [200], our best method GoogleNet_Syn_FT achieves a mAP score of 85.0%.

  5. 5.

    https://stock.adobe.com.

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Authors and Affiliations

  1. Adobe Inc., San Jose, CA, USA

    Jianming Zhang

  2. Centre for Image Analysis, Uppsala University, Uppsala, Uppsala Län, Sweden

    Filip Malmberg

  3. Department of Computer Science, Boston University, Boston, MA, USA

    Stan Sclaroff

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  1. Jianming Zhang
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  2. Filip Malmberg
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  3. Stan Sclaroff
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Zhang, J., Malmberg, F., Sclaroff, S. (2019). Salient Object Subitizing. In: Visual Saliency: From Pixel-Level to Object-Level Analysis. Springer, Cham. https://doi.org/10.1007/978-3-030-04831-0_5

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  • DOI: https://doi.org/10.1007/978-3-030-04831-0_5

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