BING: Binarized Normed Gradients for Objectness Estimation at 300fps

• Published in: 2014 IEEE Conference on Computer Vision and Pattern Recognition pp. 3286 - 3293
• Main Authors: Ming-Ming Cheng, Ziming Zhang, Wen-Yan Lin, Torr, Philip
• Format: Conference Proceeding Journal Article
• Language: English
• Published: IEEE 01.06.2014
• Subjects: binary coding; Boundaries; Color; Computation; Computer vision; Conferences; detection; Detectors; Feature extraction; Image color analysis; Norms; Object detection; Objectness; Pattern recognition; Proposals; realtime; saliency; Training; Vectors; visual attention
• ISSN: 1063-6919; 1063-6919
• DOI: 10.1109/CVPR.2014.414

Training a generic objectness measure to produce a small set of candidate object windows, has been shown to speed up the classical sliding window object detection paradigm. We observe that generic objects with well-defined closed boundary can be discriminated by looking at the norm of gradients, with a suitable resizing of their corresponding image windows in 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.). Experiments on the challenging PASCAL VOC 2007 dataset show that our method efficiently (300fps on a single laptop CPU) generates a small set of category-independent, high quality object windows, yielding 96.2% object detection rate (DR) with 1, 000 proposals. Increasing the numbers of proposals and color spaces for computing BING features, our performance can be further improved to 99.5% DR.