Article

Classification of summarized videos using hidden markov models on compressed chromaticity signatures

Authors:

Cheng Lu,

Mark S. Drew,

James AuAuthors Info & Claims

MULTIMEDIA '01: Proceedings of the ninth ACM international conference on Multimedia

Pages 479 - 482

https://doi.org/10.1145/500141.500217

Published: 01 October 2001 Publication History

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Abstract

Tools for efficiently summarizing and classifying video sequences are indispensable to assist in the synthesis and analysis of digital video. In this paper, we present a method for effective classification of different types of videos that uses the output of a concise video summarization technique that forms a list of keyframes. The summarization is produced by a method recently presented, in which we generate a universal basis on which to project a video frame feature that effectively reduces any video to the same lighting conditions. Each frame is represented by a compressed chromaticity signature. A multi-stage hierarchical clustering method efficiently summarizes any video. Here, we classify TV programs using a trained hidden Markov model, using the keyframe plus temporal features generated in the summaries.

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Alsafrjalani M(2022)A Framework Model for Integrating Social Media, the Web, and Proprietary Services Into YouTube Video Classification ProcessResearch Anthology on Applying Social Networking Strategies to Classrooms and Libraries10.4018/978-1-6684-7123-4.ch015(260-277)Online publication date: 8-Jul-2022
https://doi.org/10.4018/978-1-6684-7123-4.ch015
Grifoni PCaschera MFerri F(2020)Evaluation of a dynamic classification method for multimodal ambiguities based on Hidden Markov ModelsEvolving Systems10.1007/s12530-020-09344-3Online publication date: 23-May-2020
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Alsafrjalani M(2019)A Framework Model for Integrating Social Media, the Web, and Proprietary Services Into YouTube Video Classification ProcessInternational Journal of Multimedia Data Engineering and Management10.4018/IJMDEM.201904010210:2(21-36)Online publication date: 1-Apr-2019
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Published In

MULTIMEDIA '01: Proceedings of the ninth ACM international conference on Multimedia

October 2001

664 pages

ISBN:1581133944

DOI:10.1145/500141

Conference Chairs:
Nicolas D. Georganas
University of Ottawa
,
Radu Popescu-Zeletin
GMD Fokus

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

Publication History

Published: 01 October 2001

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MM01

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MM01: ACM Multimedia 2001

September 30 - October 5, 2001

Ottawa, Canada

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Alsafrjalani M(2022)A Framework Model for Integrating Social Media, the Web, and Proprietary Services Into YouTube Video Classification ProcessResearch Anthology on Applying Social Networking Strategies to Classrooms and Libraries10.4018/978-1-6684-7123-4.ch015(260-277)Online publication date: 8-Jul-2022
https://doi.org/10.4018/978-1-6684-7123-4.ch015
Grifoni PCaschera MFerri F(2020)Evaluation of a dynamic classification method for multimodal ambiguities based on Hidden Markov ModelsEvolving Systems10.1007/s12530-020-09344-3Online publication date: 23-May-2020
https://doi.org/10.1007/s12530-020-09344-3
Alsafrjalani M(2019)A Framework Model for Integrating Social Media, the Web, and Proprietary Services Into YouTube Video Classification ProcessInternational Journal of Multimedia Data Engineering and Management10.4018/IJMDEM.201904010210:2(21-36)Online publication date: 1-Apr-2019
https://doi.org/10.4018/IJMDEM.2019040102
Gaddampalli AZhu Q(2017)Characterization of common videos with statistical features extracted from frame transition profiles2017 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI.2017.8285277(1-7)Online publication date: Nov-2017
https://doi.org/10.1109/SSCI.2017.8285277
Kalpakis GTsikrika TMarkatopoulou FPittaras NVrochidis SMezaris VPatras IKompatsiaris I(2015)Concept Detection in Multimedia Web Resources About Home Made ExplosivesProceedings of the 2015 10th International Conference on Availability, Reliability and Security10.1109/ARES.2015.85(632-641)Online publication date: 24-Aug-2015
https://dl.acm.org/doi/10.1109/ARES.2015.85
Darabi KGhinea G(2014)Video summarization by group scoring2014 International Conference on Multimedia Computing and Systems (ICMCS)10.1109/ICMCS.2014.6911240(112-116)Online publication date: Apr-2014
https://doi.org/10.1109/ICMCS.2014.6911240
Chen HChen H(2011)Extremist YouTube VideosDark Web10.1007/978-1-4614-1557-2_15(295-318)Online publication date: 7-Nov-2011
https://doi.org/10.1007/978-1-4614-1557-2_15
Zhou HSadka ASwash MAzizi JSadiq U(2010)Feature extraction and clustering for dynamic video summarisationNeurocomputing10.1016/j.neucom.2009.09.02273:10-12(1718-1729)Online publication date: Jun-2010
https://doi.org/10.1016/j.neucom.2009.09.022
Huang CFu TChen H(2010)Text‐based video content classification for online video‐sharing sitesJournal of the American Society for Information Science and Technology10.1002/asi.2129161:5(891-906)Online publication date: 29-Jan-2010
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Oh J(2009)Video Data MiningEncyclopedia of Data Warehousing and Mining, Second Edition10.4018/978-1-60566-010-3.ch312(2042-2047)Online publication date: 2009
https://doi.org/10.4018/978-1-60566-010-3.ch312
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