@article{Cheng2019, 
author = {Ming-Ming Cheng and Yun Liu and Wen-Yan Lin and Ziming Zhang and Paul L. Rosin and Philip H. S. Torr},
title = {BING: Binarized normed gradients for objectness estimation at 300fps},
year = {2019},
journal = {Computational Visual Media},
volume = {5},
number = {1},
pages = {3-20},
keywords = {object proposals, objectness, visual atten-tion, category agnostic proposals},
url = {https://www.sciopen.com/article/10.1007/s41095-018-0120-1},
doi = {10.1007/s41095-018-0120-1},
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.}
}