NDLI: Coarse-to-Fine Semantic Video Segmentation Using Supervoxel Trees

Content Provider	IEEE Xplore Digital Library
Author	Jain, A. Chatterjee, S. Vidal, R.
Copyright Year	2013
Description	Author affiliation: Univ. of California, Berkeley, Berkeley, CA, USA (Chatterjee, S.)
Abstract	We propose an exact, general and efficient coarse-to-fine energy minimization strategy for semantic video segmentation. Our strategy is based on a hierarchical abstraction of the supervoxel graph that allows us to minimize an energy defined at the finest level of the hierarchy by minimizing a series of simpler energies defined over coarser graphs. The strategy is exact, i.e., it produces the same solution as minimizing over the finest graph. It is general, i.e., it can be used to minimize any energy function (e.g., unary, pair wise, and higher-order terms) with any existing energy minimization algorithm (e.g., graph cuts and belief propagation). It also gives significant speedups in inference for several datasets with varying degrees of spatio-temporal continuity. We also discuss the strengths and weaknesses of our strategy relative to existing hierarchical approaches, and the kinds of image and video data that provide the best speedups.
Sponsorship	IEEE Comput. Soc.
Starting Page	1865
Ending Page	1872
File Size	1016088
Page Count	8
File Format	PDF
ISBN	9781479928408
ISSN	15505499
DOI	10.1109/ICCV.2013.234
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-01
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Labeling Inference algorithms Image segmentation Radio frequency Belief propagation Optimization Minimization energy minimization Video segmentation hierarchical inference coarse-to-fine inference
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

2013 ieee international conference on computer vision coarse-to-fine semantic video segmentation using supervoxel trees.

A Principled Approach for Coarse-to-Fine MAP Inference

Associative hierarchical CRFs for object class image segmentation

Optical flow estimation on coarse-to-fine region-trees using discrete optimization

Alphabet SOUP: A framework for approximate energy minimization

Graph-shifts: Natural image labeling by dynamic hierarchical computing

$(BP)^{2}:$ Beyond pairwise Belief Propagation labeling by approximating Kikuchi free energies

Efficient belief propagation for early vision

Learning Layered Motion Segmentations of Video

Coarse-to-Fine Semantic Video Segmentation Using Supervoxel Trees

Similar Documents

2013 ieee international conference on computer vision coarse-to-fine semantic video segmentation using supervoxel trees.

A Principled Approach for Coarse-to-Fine MAP Inference

Associative hierarchical CRFs for object class image segmentation

Optical flow estimation on coarse-to-fine region-trees using discrete optimization

Alphabet SOUP: A framework for approximate energy minimization

Graph-shifts: Natural image labeling by dynamic hierarchical computing

$(BP)^{2}:$ Beyond pairwise Belief Propagation labeling by approximating Kikuchi free energies

Efficient belief propagation for early vision

Learning Layered Motion Segmentations of Video

Coarse-to-Fine Semantic Video Segmentation Using Supervoxel Trees