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  1. Proceedings of the 1st ACM international workshop on Events in multimedia (EiMM '09)
  2. Query-based video event definition using rough set theory
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Connecting the real world with the ubiquitous overlay in ambient media
Speeding up spatio-temporal sliding-window search for efficient event detection in crowded videos
Unifying and targeting cultural activities via events modelling and profiling
A hierarchical model for representation of events in multimedia observation systems
Query-based video event definition using rough set theory
An approach based on events for treating the late tuning problem in interactive live TV shows
Event composition operators: ECO
Event detection in sports video based on generative-discriminative models
An investigation into event decay from large personal media archives
Combining ship trajectories and semantics with the simple event model (SEM)
Ice hockey shot event modeling with mixture hidden Markov model

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Query-based video event definition using rough set theory

Content Provider ACM Digital Library
Author Sugihara, Chieri Shirahama, Kimiaki Uehara, Kuniaki Matsuoka, Yuta
Abstract Since events queried by a user range much widely, indexing a video archive with pre-defined events is impractical. Hence, "query-based event definition" is an essential technique where an event is defined from example videos provided by the user. In this paper, we introduce a novel query-based event definition method to cover a large variation of low-level features in the same event. Specifically, due to arbitrary video production techniques, shots of the same event contain significantly different low-level features. Thus, we assume that these shots are distributed in different subsets in a feature space. To extract such subsets, we apply "rough set theory" to example shots relevant to an event (positive examples) and irrelevant example shots (negative examples). Thereby, we can extract different subsets where positive or negative examples can be correctly classified by "decision rules" consisting of specific low-level features. In this process, to avoid extracting over-specialized decision rules, we distinguish important low-level features from unimportant ones using "Multiple Correspondence Analysis (MCA)". Finally, the video archive is searched based on decision rules. Experimental results on TRECVID 2008 video archive show the possibility of our method to achieve the wide coverage of each event.
Starting Page 9
Ending Page 16
Page Count 8
File Format PDF
ISBN 9781605587547
DOI 10.1145/1631024.1631029
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2009-10-23
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Query-based event definition Rough set theory Video archive Event retrieval Multiple correspondence analysis
Content Type Text
Resource Type Article
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