research-article

An effective method for video genre classification

Authors:

Jian-Feng Chen,

Xiangyang XueAuthors Info & Claims

CIVR '10: Proceedings of the ACM International Conference on Image and Video Retrieval

Pages 97 - 104

https://doi.org/10.1145/1816041.1816058

Published: 05 July 2010 Publication History

Abstract

As a common media type, video is closely bound up with our life. Since the number and the kinds of videos increase steadily, how to organize the enormous amount of videos and obtain the content of interest has become an important research issue. And the video analysis system emerges, also the research of video gene classification has become an important topic.

This paper focuses on classification on video genres of cartoons, movies, advertisements, news, and sports. It can be served for video organization, retrieval, etc. Based on the analysis on different video genres, we fuse video's time feature and color feature from shots together. Specifically, there are seven features including gradient and color features and each one could be an expert for some genre of video. We select these expert features and let them collaborate to improve the accuracy of classification. Then support vector machine (SVM) is used for classification. Experimental results on large amount of video demonstrate the effectiveness of the proposed method.

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Cited By

Martins GAlmeida JPapa J(2022)Supervised Video Genre Classification Using Optimum-Path ForestProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications10.1007/978-3-319-25751-8_88(735-742)Online publication date: 11-Mar-2022
https://dl.acm.org/doi/10.1007/978-3-319-25751-8_88
Verma CDey S(2015)Methods to Obtain Training Videos for Fully Automated Application-Specific ClassificationIEEE Access10.1109/ACCESS.2015.24611563(1188-1205)Online publication date: 2015
https://doi.org/10.1109/ACCESS.2015.2461156
Verma CDey S(2013)Fully Automated Learning for Application-Specific Web Video ClassificationProceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 0110.1109/WI-IAT.2013.44(307-314)Online publication date: 17-Nov-2013
https://dl.acm.org/doi/10.1109/WI-IAT.2013.44
Show More Cited By

Index Terms

An effective method for video genre classification
1. Information systems
  1. Information retrieval

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Published In

cover image ACM Conferences

CIVR '10: Proceedings of the ACM International Conference on Image and Video Retrieval

July 2010

492 pages

ISBN:9781450301176

DOI:10.1145/1816041

Conference Chairs:
Shipeng Li
Microsoft Research Asia, China
,
Xinbo Gao
Xidian University, China
,
Nicu Sebe
University of Trento, Italy

Copyright © 2010 ACM.

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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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 July 2010

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CIVR' 10

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SIGMM

CIVR' 10: International Conference on Image and Video Retrieval

July 5 - 7, 2010

Xi'an, China

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Cited By

Martins GAlmeida JPapa J(2022)Supervised Video Genre Classification Using Optimum-Path ForestProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications10.1007/978-3-319-25751-8_88(735-742)Online publication date: 11-Mar-2022
https://dl.acm.org/doi/10.1007/978-3-319-25751-8_88
Verma CDey S(2015)Methods to Obtain Training Videos for Fully Automated Application-Specific ClassificationIEEE Access10.1109/ACCESS.2015.24611563(1188-1205)Online publication date: 2015
https://doi.org/10.1109/ACCESS.2015.2461156
Verma CDey S(2013)Fully Automated Learning for Application-Specific Web Video ClassificationProceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 0110.1109/WI-IAT.2013.44(307-314)Online publication date: 17-Nov-2013
https://dl.acm.org/doi/10.1109/WI-IAT.2013.44
Wei APei YZha H(2012)Random-sampling-based spatial-temporal feature for consumer video concept classification2012 19th IEEE International Conference on Image Processing10.1109/ICIP.2012.6467246(1861-1864)Online publication date: Sep-2012
https://doi.org/10.1109/ICIP.2012.6467246

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