NDLI: A general framework for temporal video scene segmentation

Content Provider	IEEE Xplore Digital Library
Author	Yun Zhai Shah, M.
Copyright Year	2005
Description	Author affiliation: Sch. of Comput. Sci., Central Florida Univ. (Yun Zhai; Shah, M.)
Abstract	Videos are composed of many shots caused by different camera operations, e.g., on/off operations and switching between cameras. One important goal in video analysis is to group the shots into temporal scenes, such that all the shots in a single scene are related to a particular physical setting, an on-going action or a theme. In this paper, we present a general framework for temporal scene segmentation for various video types. The proposed method is formulated in a statistical fashion and uses the Markov chain Monte Carlo (MCMC) technique to determine the boundaries between video scenes. In this approach, an arbitrary number of scene boundaries are randomly initialized and automatically updated using two types of updates: diffuse and jumps. The posterior probability on the number of scenes and their boundary locations is computed based on the model priors and the data likelihood. The updates of the model parameters are controlled by the hypothesis ratio test in the MCMC process. The proposed framework has been experimented on two types of videos, home videos and feature films, and accurate results have been obtained
Sponsorship	IEEE Comput. Soc. Tech. Comm. on Pattern Anal. and Machine Intelligence
Starting Page	1111
Ending Page	1116
File Size	275155
Page Count	6
File Format	PDF
ISBN	076952334X
ISSN	15505499
DOI	10.1109/ICCV.2005.6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Layout Cameras Motion pictures Production Computer science Multimedia communication TV broadcasting Hidden Markov models Monte Carlo methods Probability
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

Scene Segmentation and Categorization Using NCuts

Segmental Hidden Markov Models for View-based Sport Video Analysis

Video scene segmentation using Markov chain Monte Carlo

Automatic Segmentation of Home Videos

Video clip recognition using joint audio-visual processing model

EMS: Energy Minimization Based Video Scene Segmentation

Scene detection in Hollywood movies and TV shows

Audiotory Movie Summarization by Detecting Scene Changes and Sound Events

Video clip representation and recognition using composite shot models

A general framework for temporal video scene segmentation

Similar Documents

Scene Segmentation and Categorization Using NCuts

Segmental Hidden Markov Models for View-based Sport Video Analysis

Video scene segmentation using Markov chain Monte Carlo

Automatic Segmentation of Home Videos

Video clip recognition using joint audio-visual processing model

EMS: Energy Minimization Based Video Scene Segmentation

Scene detection in Hollywood movies and TV shows

Audiotory Movie Summarization by Detecting Scene Changes and Sound Events

Video clip representation and recognition using composite shot models

A general framework for temporal video scene segmentation