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Research Article | Open Access

BING: Binarized normed gradients for objectness estimation at 300fps

Ming-Ming Cheng1( ), Yun Liu1, Wen-Yan Lin2, Ziming Zhang3, Paul L. Rosin4, Philip H. S. Torr5
CCS, Nankai University, Tianjin 300350, China.
Institute for Infocomm Research, Singapore, 138632.
MERL, Cambridge, MA 02139-1955, US.
Cardiff University, Wales, CF24 3AA, UK.
University of Oxford, Oxford, OX1 3PJ, UK.

* These authors contributed equally to this work.

Show Author Information

Abstract

Training a generic objectness measure to produce object proposals has recently become of significant interest. We observe that generic objects with well-defined closed boundaries can be detected by looking at the norm of gradients, with a suitable resizing of their corresponding image windows to a small fixed size. Based on this observation and computational reasons, we propose to resize the window to 8×8 and use the norm of the gradients as a simple 64D feature to describe it, for explicitly training a generic objectness measure. We further show how the binarized version of this feature, namely binarized normed gradients (BING), can be used for efficient objectness estimation, which requires only a few atomic operations (e.g., add, bitwise shift, etc.). To improve localization quality of the proposals while maintaining efficiency, we propose a novel fast segmentation method and demonstrate its effectiveness for improving BING’s localization performance, when used in multi-thresholding straddling expansion (MTSE) post-processing. On the challenging PASCAL VOC2007 dataset, using 1000 proposals per image and intersection-over-union threshold of 0.5, our proposal method achieves a 95.6% object detection rate and 78.6% mean average best overlap in less than 0.005 second per image.

Keywords

object proposalsobjectnessvisual atten-tioncategory agnostic proposals

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Computational Visual Media
Volume 5 Issue 1,
March 2019
Pages 3-20
DOI: 10.1007/s41095-018-0120-1
Cite this article:
Cheng M-M, Liu Y, Lin W-Y, et al. BING: Binarized normed gradients for objectness estimation at 300fps. Computational Visual Media, 2019, 5(1): 3-20. https://doi.org/10.1007/s41095-018-0120-1

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Revised: 08 May 2018
Accepted: 26 May 2018
Published: 08 April 2019
© The author(s) 2018

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