Large-scale news topic tracking and key-scene ranking with video near-duplicate constraints

Authors:
Xiaomeng Wu;Ichiro Ide;Shin'ichi Satoh
Affiliations:
National Institute of Informatics, Tokyo, Japan;Graduate School of I.S., Nagoya University, Nagoya, Japan;National Institute of Informatics, Tokyo, Japan
Venue:
LS-MMRM '09 Proceedings of the First ACM workshop on Large-scale multimedia retrieval and mining
Year:
2009

Citing 10
Cited 2

Query based event extraction along a timeline

Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
A graph-theoretic approach to extract storylines from search results

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Towards auto-documentary: tracking the evolution of news stories

Proceedings of the 12th annual ACM international conference on Multimedia
Tracking news stories across different sources

Proceedings of the 13th annual ACM international conference on Multimedia
Fast tracking of near-duplicate keyframes in broadcast domain with transitivity propagation

MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
mediaWalker: a video archive explorer based on time-series semantic structure

Proceedings of the 15th international conference on Multimedia
Storyline-based summarization for news topic retrospection

Decision Support Systems
trackThem: exploring a large-scale news video archive by tracking human relations

AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
Key image extraction from a news video archive for visualizing its semantic structure

PCM'04 Proceedings of the 5th Pacific Rim conference on Advances in Multimedia Information Processing - Volume Part I
Multimodal News Story Clustering With Pairwise Visual Near-Duplicate Constraint

IEEE Transactions on Multimedia

News story clustering from both what and how aspects: using bag of word model and affinity propagation

AIEMPro '11 Proceedings of the 2011 ACM international workshop on Automated media analysis and production for novel TV services
A unified framework for web video topic discovery and visualization

Pattern Recognition Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

To make full use of the overwhelming volume of news videos available today, it is necessary to track the development of news stories from different channels, mine their dependencies, and organize them in a semantic way. We propose a novel news topic tracking and re-ranking system. The main contributions include: (1) a novel scheme of mining topic-related stories through tracking and re-ranking on the basis of near duplicates built on top of text, (2) a proposed simple but effective query-expansion algorithm for improving the representativeness of a search query, (3) a large-scale broadcast video database containing more than 34,000 news stories constructed for experimentation, and (4) a novel key-scene ranking scheme for analyzing both text similarity and video near-duplicate constraints.