BING: Binarized normed gradients for objectness estimation at 300fps

• Main Authors: CHENG, Ming-Ming, LIU, Yun, LIN, Wen-yan, ZHANG, Ziming, ROSIN, Paul L., TORR, Philip H. S.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: visual attention; category agnostic proposals; object proposals; objectness; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/4716; https://ink.library.smu.edu.sg/context/sis_research/article/5719/viewcontent/ObjectnessBING.pdf
• Institution: Singapore Management University

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.