CVPR 2014 Video Spotlights
TechTalks from event: CVPR 2014 Video Spotlights
Orals 4B : Recognition: Detection, Categorization, Classification
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BING: Binarized Normed Gradients for Objectness Estimation at 300fpsTraining 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.
- All Sessions
- Orals 1A : Matching & Reconstruction
- Orals 1B : Segmentation & Grouping
- Posters 1A : Recognition, Segmentation, Stereo & SFM
- Orals 1C : Statistical Methods & Learning I
- Orals 1D : Action Recognition
- Posters 1B : 3D Vision, Action Recognition, Recognition, Statistical Methods & Learning
- Orals 2A : Motion & Tracking
- Orals 2B : Discrete Optimization
- Posters 2A : Motion & Tracking, Optimization, Statistical Methods & Learning, Stereo & SFM
- Posters 2B : Face & Gesture, Recognition
- Orals 3A : Physics-Based Vision & Shape-from-X
- Orals 3B : Video: Events, Activities & Surveillance
- Posters 3A : Physics-Based Vision, Recognition, Video: Events, Activities & Surveillance
- Orals 3C : Medical & Biological Image Analysis
- Orals 3D : Low-Level Vision & Image Processing
- Posters 3B : Biologically Inspired Vision, Low-Level Vision, Medical & Biological Image Analysis, Segmentation
- Orals 4A : Computational Photography: Sensing and Display
- Orals 4B : Recognition: Detection, Categorization, Classification
- Posters 4A : Computational Photography, Motion & Tracking, Recognition
- Orals 4C : 3D Geometry & Shape
- Orals 4F : View Synthesis & Other Applications
- Posters 4B : 3D Vision, Document Analysis, Optimization Methods, Shape, Vision for Graphics, Web & Vision Systems
- Orals 2F : Convolutional Neural Networks