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

Additional URL

https://doi.org/10.1109/CVPR.2014.414

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