BING: Binarized normed gradients for objectness estimation at 300fps
Publication Type
Conference Proceeding Article
Publication Date
6-2014
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 multithresholding straddling expansion (MTSE) postprocessing. On the challenging PASCAL VOC2007 dataset, using 1000 proposals per image and intersectionover-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 proposals, objectness, visual attention, category agnostic proposal
Discipline
Graphics and Human Computer Interfaces
Research Areas
Data Science and Engineering
Publication
Proceedings of the 27th IEEE Conference on Computer Vision and Pattern Recognition, Columbus, 2014 June 23-28
Volume
5
First Page
3286
Last Page
3293
ISBN
9781479951178
Identifier
10.1109/CVPR.2014.414
Publisher
IEEE Computer Society
City or Country
Columbus
Citation
CHENG, Ming-Ming; LIU, Yun; LIN, Wen-yan; ZHANG, Ziming; and ROSIN, Paul L. TORR.
BING: Binarized normed gradients for objectness estimation at 300fps. (2014). Proceedings of the 27th IEEE Conference on Computer Vision and Pattern Recognition, Columbus, 2014 June 23-28. 5, 3286-3293.
Available at: https://ink.library.smu.edu.sg/sis_research/4803
Additional URL
https://doi.org/10.1109/CVPR.2014.414