CVPR 2014 Video Spotlights
TechTalks from event: CVPR 2014 Video Spotlights
Orals 2B : Discrete Optimization
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Scene Labeling Using Beam Search Under Mutex ConstraintsThis paper addresses the problem of assigning object class labels to image pixels. Following recent holistic formulations, we cast scene labeling as inference of a conditional random field (CRF) grounded onto superpixels. The CRF inference is specified as quadratic program (QP) with mutual exclusion (mutex) constraints on class label assignments. The QP is solved using a beam search (BS), which is well-suited for scene labeling, because it explicitly accounts for spatial extents of objects; conforms to inconsistency constraints from domain knowledge; and has low computational costs. BS gradually builds a search tree whose nodes correspond to candidate scene labelings. Successor nodes are repeatedly generated from a select set of their parent nodes until convergence. We prove that our BS efficiently maximizes the QP objective of CRF inference. Effectiveness of our BS for scene labeling is evaluated on the benchmark MSRC, Stanford Backgroud, PASCAL VOC 2009 and 2010 datasets.
- 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