NDLI: A unified framework for event summarization and rare event detection

Content Provider	IEEE Xplore Digital Library
Author	Junseok Kwon Kyoung Mu Lee
Copyright Year	2012
Description	Author affiliation: Department of EECS, ASRI, Seoul National University, 151-742, Seoul, Korea (Junseok Kwon; Kyoung Mu Lee)
Abstract	A novel approach for event summarization and rare event detection is proposed. Unlike conventional methods that deal with event summarization and rare event detection independently, we solve them together by transforming the problems into a graph editing framework. In our approach, a video is represented as a graph, in which each node of the graph indicates an event obtained by segmenting the video spatially and temporally, while edges between nodes describe the events related to each other. Based on the degree of relations, edges have different weights. After learning the graph structure, our method edits the graph by merging its subgraphs or pruning its edges. The graph is edited toward minimizing a predefined energy model with the Data-Driven Markov Chain Monte Carlo method. The energy model consists of several parameters that represent causality, frequency, and significance of events. We design a specific energy model utilizing these parameters to satisfy each objective of event summarization and rare event detection. Experimental results show that the proposed approach accurately summarizes a video in a fully unsupervised manner. Moreover, the experiments also demonstrate that the approach is advantageous in detecting the rare transition of events.
Starting Page	1266
Ending Page	1273
File Size	474556
Page Count	8
File Format	PDF
ISBN	9781467312264
ISSN	10636919
e-ISBN	9781467312288
e-ISBN	9781467312271
DOI	10.1109/CVPR.2012.6247810
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Event detection Proposals Frequency measurement Density functional theory Data mining Markov processes Hidden Markov models
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

A Video Event Detection and Mining Framework

A Unified Framework for Event Summarization and Rare Event Detection from Multiple Views

Semi-supervised adapted HMMs for unusual event detection

Gesture tracking and recognition for lecture video editing

Infinite Hidden Markov Models and ISA Features for Unusual-Event Detection in Video

Unusual Event Detection via Multi-camera Video Mining

Temporal Sequence Modeling for Video Event Detection

Facial event mining using coupled hidden Markov models

Video mining: pattern discovery versus pattern recognition

A unified framework for event summarization and rare event detection

Similar Documents

A Video Event Detection and Mining Framework

A Unified Framework for Event Summarization and Rare Event Detection from Multiple Views

Semi-supervised adapted HMMs for unusual event detection

Gesture tracking and recognition for lecture video editing

Infinite Hidden Markov Models and ISA Features for Unusual-Event Detection in Video

Unusual Event Detection via Multi-camera Video Mining

Temporal Sequence Modeling for Video Event Detection

Facial event mining using coupled hidden Markov models

Video mining: pattern discovery versus pattern recognition

A unified framework for event summarization and rare event detection