Large-scale multimedia data mining using MapReduce framework

Hanli Wang; Yun Shen; Lei Wang; Kuangtian Zhufeng; Wei Wang; Cheng Cheng

doi:10.1109/CloudCom.2012.6427595

Large-scale multimedia data mining using MapReduce framework

Hanli Wang, Yun Shen, Lei Wang, Kuangtian Zhufeng, Wei Wang, Cheng Cheng

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

19 Scopus citations

Abstract

In this paper, the framework of MapReduce is explored for large-scale multimedia data mining. Firstly, a brief overview of MapReduce and Hadoop is presented to speed up large-scale multimedia data mining. Then, the high-level theory and low-level implementation for several key computer vision technologies involved in this work are introduced, such as 2D/3D interest point detection, clustering, bag of features, and so on. Experimental results on image classification, video event detection and near-duplicate video retrieval are carried out on a five-node Hadoop cluster to demonstrate the efficiency of the proposed MapReduce framework for large-scale multimedia data mining applications.

Original language	English
Title of host publication	CloudCom 2012 - Proceedings
Subtitle of host publication	2012 4th IEEE International Conference on Cloud Computing Technology and Science
Publisher	IEEE Computer Society
Pages	287-292
Number of pages	6
ISBN (Print)	9781467345095
DOIs	https://doi.org/10.1109/CloudCom.2012.6427595
State	Published - 2012
Externally published	Yes
Event	2012 4th IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2012 - Taipei, Taiwan, Province of China Duration: 3 Dec 2012 → 6 Dec 2012

Publication series

Name	CloudCom 2012 - Proceedings: 2012 4th IEEE International Conference on Cloud Computing Technology and Science

Conference

Conference	2012 4th IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2012
Country/Territory	Taiwan, Province of China
City	Taipei
Period	3/12/12 → 6/12/12

Keywords

Hadoop
Image classification
MapReduce
Near-duplicate video retrieval
Video event detection

Access to Document

10.1109/CloudCom.2012.6427595

Cite this

Wang, H., Shen, Y., Wang, L., Zhufeng, K., Wang, W., & Cheng, C. (2012). Large-scale multimedia data mining using MapReduce framework. In CloudCom 2012 - Proceedings: 2012 4th IEEE International Conference on Cloud Computing Technology and Science (pp. 287-292). Article 6427595 (CloudCom 2012 - Proceedings: 2012 4th IEEE International Conference on Cloud Computing Technology and Science). IEEE Computer Society. https://doi.org/10.1109/CloudCom.2012.6427595

Wang, Hanli ; Shen, Yun ; Wang, Lei et al. / Large-scale multimedia data mining using MapReduce framework. CloudCom 2012 - Proceedings: 2012 4th IEEE International Conference on Cloud Computing Technology and Science. IEEE Computer Society, 2012. pp. 287-292 (CloudCom 2012 - Proceedings: 2012 4th IEEE International Conference on Cloud Computing Technology and Science).

@inproceedings{e4b76165f1ce4b25b298efd2f6785742,

title = "Large-scale multimedia data mining using MapReduce framework",

abstract = "In this paper, the framework of MapReduce is explored for large-scale multimedia data mining. Firstly, a brief overview of MapReduce and Hadoop is presented to speed up large-scale multimedia data mining. Then, the high-level theory and low-level implementation for several key computer vision technologies involved in this work are introduced, such as 2D/3D interest point detection, clustering, bag of features, and so on. Experimental results on image classification, video event detection and near-duplicate video retrieval are carried out on a five-node Hadoop cluster to demonstrate the efficiency of the proposed MapReduce framework for large-scale multimedia data mining applications.",

keywords = "Hadoop, Image classification, MapReduce, Near-duplicate video retrieval, Video event detection",

author = "Hanli Wang and Yun Shen and Lei Wang and Kuangtian Zhufeng and Wei Wang and Cheng Cheng",

year = "2012",

doi = "10.1109/CloudCom.2012.6427595",

language = "英语",

isbn = "9781467345095",

series = "CloudCom 2012 - Proceedings: 2012 4th IEEE International Conference on Cloud Computing Technology and Science",

publisher = "IEEE Computer Society",

pages = "287--292",

booktitle = "CloudCom 2012 - Proceedings",

address = "美国",

note = "2012 4th IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2012 ; Conference date: 03-12-2012 Through 06-12-2012",

}

Wang, H, Shen, Y, Wang, L, Zhufeng, K, Wang, W & Cheng, C 2012, Large-scale multimedia data mining using MapReduce framework. in CloudCom 2012 - Proceedings: 2012 4th IEEE International Conference on Cloud Computing Technology and Science., 6427595, CloudCom 2012 - Proceedings: 2012 4th IEEE International Conference on Cloud Computing Technology and Science, IEEE Computer Society, pp. 287-292, 2012 4th IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2012, Taipei, Taiwan, Province of China, 3/12/12. https://doi.org/10.1109/CloudCom.2012.6427595

Large-scale multimedia data mining using MapReduce framework. / Wang, Hanli; Shen, Yun; Wang, Lei et al.
CloudCom 2012 - Proceedings: 2012 4th IEEE International Conference on Cloud Computing Technology and Science. IEEE Computer Society, 2012. p. 287-292 6427595 (CloudCom 2012 - Proceedings: 2012 4th IEEE International Conference on Cloud Computing Technology and Science).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - In this paper, the framework of MapReduce is explored for large-scale multimedia data mining. Firstly, a brief overview of MapReduce and Hadoop is presented to speed up large-scale multimedia data mining. Then, the high-level theory and low-level implementation for several key computer vision technologies involved in this work are introduced, such as 2D/3D interest point detection, clustering, bag of features, and so on. Experimental results on image classification, video event detection and near-duplicate video retrieval are carried out on a five-node Hadoop cluster to demonstrate the efficiency of the proposed MapReduce framework for large-scale multimedia data mining applications.

KW - Hadoop

KW - Image classification

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KW - Near-duplicate video retrieval

KW - Video event detection

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Wang H, Shen Y, Wang L, Zhufeng K, Wang W, Cheng C. Large-scale multimedia data mining using MapReduce framework. In CloudCom 2012 - Proceedings: 2012 4th IEEE International Conference on Cloud Computing Technology and Science. IEEE Computer Society. 2012. p. 287-292. 6427595. (CloudCom 2012 - Proceedings: 2012 4th IEEE International Conference on Cloud Computing Technology and Science). doi: 10.1109/CloudCom.2012.6427595

Large-scale multimedia data mining using MapReduce framework

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Keywords

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