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VideoGraph: a new tool for video mining and classification

Published: 01 January 2001 Publication History

Abstract

This paper introduces Videograph, a new tool for video mining and visu alizing the structure of the plot of a video sequence. The main idea is to “stitch” together similar scenes which are apart in time. We give a fast algorithm to do stitching and we show case studies, where our approach (a) gives good features for classification (91% accuracy), and (b) results in Videographs which reveal the logical structure of the plot of the video clips.

References

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C. Faloutsos and K.-I. Lin. Fastmap: A fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets. Proceedings of the ACM SIGMOD Conference, pages 163-174, 1995.
[2]
V. Kobla, D. Doermann, and C. Faloutsos. Videotrails: Representing and visualizing structure in video sequences. Proceeding of the Fifth ACM International Multimedia Conference, pages 335-346, November 1997.
[3]
R. Kohavi, D. Sommerfield, and J. Dougherty. Data mining using MLC++: A machine learning library in C++. Tools with Artificial Intelligence, http://www.sgi.com/Technology/mlc.
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H. Wactlar, M. Christel, Y. Gong, and A. Hauptmann. Lessons learned from the creation and deployment ofa terabyte digital video library. IEEE Computer, 32(2):66-73, February 1999.

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cover image ACM Conferences
JCDL '01: Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries
January 2001
481 pages
ISBN:1581133456
DOI:10.1145/379437
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 01 January 2001

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JCDL '01 Paper Acceptance Rate 76 of 250 submissions, 30%;
Overall Acceptance Rate 415 of 1,482 submissions, 28%

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  • (2016)Towards large-scale multimedia retrieval enriched by knowledge about human interpretationMultimedia Tools and Applications10.1007/s11042-014-2292-875:1(297-331)Online publication date: 1-Jan-2016
  • (2009)Video Data MiningEncyclopedia of Data Warehousing and Mining, Second Edition10.4018/978-1-60566-010-3.ch312(2042-2047)Online publication date: 2009
  • (2007)A Time-Constrained Sequential Pattern Mining for Extracting Semantic Events in VideosMultimedia Data Mining and Knowledge Discovery10.1007/978-1-84628-799-2_20(404-426)Online publication date: 2007
  • (2006)An empirical investigation into user navigation of digital video using the VCR-like control setInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2005.08.01264:4(340-355)Online publication date: 1-Apr-2006
  • (2005)Effective image and video miningProceedings of the 6th international workshop on Multimedia data mining: mining integrated media and complex data10.1145/1133890.1133895(43-52)Online publication date: 21-Aug-2005
  • (2005)Modelling VCR-like video content navigationDisplays10.1016/j.displa.2005.02.00326:2(79-96)Online publication date: Apr-2005
  • (2004)Video Indexing and Abstraction for RetrievalContent-Based Analysis of Digital Video10.1007/1-4020-8115-4_4(107-141)Online publication date: 2004

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