Please use this identifier to cite or link to this item: https://doi.org/10.1145/1873951.1873967
Title: A generic framework for event detection in various video domains
Authors: Zhang, T.
Xu, C.
Zhu, G. 
Liu, S. 
Lu, H.
Keywords: broadcast video
event detection
internet
multiple instance learning
semi-supervised learning
web-casting text
Issue Date: 2010
Source: Zhang, T.,Xu, C.,Zhu, G.,Liu, S.,Lu, H. (2010). A generic framework for event detection in various video domains. MM'10 - Proceedings of the ACM Multimedia 2010 International Conference : 103-112. ScholarBank@NUS Repository. https://doi.org/10.1145/1873951.1873967
Abstract: Event detection is essential for the extensively studied video analysis and understanding area. Although various approaches have been proposed for event detection, there is a lack of a generic event detection framework that can be applied to various video domains (e.g. sports, news, movies, surveillance). In this paper, we present a generic event detection approach based on semi-supervised learning and Internet vision. Concretely, a Graph-based Semi-Supervised Multiple Instance Learning (GSSMIL) algorithm is proposed to jointly explore small-scale expert labeled videos and large-scale unlabeled videos to train the event models to detect video event boundaries. The expert labeled videos are obtained from the analysis and alignment of well-structured video related text (e.g. movie scripts, web-casting text, close caption). The unlabeled data are obtained by querying related events from the video search engine (e.g. YouTube) in order to give more distributive information for event modeling. A critical issue of GSSMIL in constructing a graph is the weight assignment, where the weight of an edge specifies the similarity between two data points. To tackle this problem, we propose a novel Multiple Instance Learning Induced Similarity (MILIS) measure by learning instance sensitive classifiers. We perform the thorough experiments in three popular video domains: movies, sports and news. The results compared with the state-of-the-arts are promising and demonstrate our proposed approach is performance-effective. © 2010 ACM.
Source Title: MM'10 - Proceedings of the ACM Multimedia 2010 International Conference
URI: http://scholarbank.nus.edu.sg/handle/10635/68823
ISBN: 9781605589336
DOI: 10.1145/1873951.1873967
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

19
checked on Dec 13, 2017

Page view(s)

36
checked on Dec 9, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.