Hierarchical temporal association mining for video event detection in video databases

Min Chen, Shu Ching Chen, Mei-Ling Shyu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

22 Citations (Scopus)

Abstract

With the proliferation of multimedia data and ever-growing requests for multimedia applications, new challenges are emerged for efficient and effective managing and accessing large audio-visual collections. In this paper, we present a novel framework for video event detection, which plays an essential role in high-level video indexing and retrieval. Especially, since temporal information in a video sequence is critical in conveying video content, a hierarchical temporal association mining approach is developed to systematically capture the characteristic temporal patterns with respect to the events of interest. In this process, the unique challenges caused by the loose video structure and skewed data distribution issues are effectively tackled. In addition, an adaptive mechanism is proposed to determine the essential thresholds which are generally defined manually in the traditional association rule mining (ARM) approach. This framework thus largely relaxes the dependence on the domain knowledge and contributes to the ultimate goal of automatic video content analysis.

Original languageEnglish
Title of host publicationProceedings - International Conference on Data Engineering
Pages137-145
Number of pages9
DOIs
StatePublished - Dec 1 2007
EventWorkshops in Conjunction with the 23rd International Conference on Data Engineering - ICDE 2007 - Istanbul, Turkey
Duration: Apr 15 2007Apr 20 2007

Other

OtherWorkshops in Conjunction with the 23rd International Conference on Data Engineering - ICDE 2007
CountryTurkey
CityIstanbul
Period4/15/074/20/07

Fingerprint

Association rules
Conveying

ASJC Scopus subject areas

  • Software
  • Engineering(all)
  • Engineering (miscellaneous)

Cite this

Chen, M., Chen, S. C., & Shyu, M-L. (2007). Hierarchical temporal association mining for video event detection in video databases. In Proceedings - International Conference on Data Engineering (pp. 137-145). [4400983] https://doi.org/10.1109/ICDEW.2007.4400983

Hierarchical temporal association mining for video event detection in video databases. / Chen, Min; Chen, Shu Ching; Shyu, Mei-Ling.

Proceedings - International Conference on Data Engineering. 2007. p. 137-145 4400983.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Chen, M, Chen, SC & Shyu, M-L 2007, Hierarchical temporal association mining for video event detection in video databases. in Proceedings - International Conference on Data Engineering., 4400983, pp. 137-145, Workshops in Conjunction with the 23rd International Conference on Data Engineering - ICDE 2007, Istanbul, Turkey, 4/15/07. https://doi.org/10.1109/ICDEW.2007.4400983
Chen M, Chen SC, Shyu M-L. Hierarchical temporal association mining for video event detection in video databases. In Proceedings - International Conference on Data Engineering. 2007. p. 137-145. 4400983 https://doi.org/10.1109/ICDEW.2007.4400983
Chen, Min ; Chen, Shu Ching ; Shyu, Mei-Ling. / Hierarchical temporal association mining for video event detection in video databases. Proceedings - International Conference on Data Engineering. 2007. pp. 137-145
@inproceedings{e2fd24c5b87f4b149d12834281515818,
title = "Hierarchical temporal association mining for video event detection in video databases",
abstract = "With the proliferation of multimedia data and ever-growing requests for multimedia applications, new challenges are emerged for efficient and effective managing and accessing large audio-visual collections. In this paper, we present a novel framework for video event detection, which plays an essential role in high-level video indexing and retrieval. Especially, since temporal information in a video sequence is critical in conveying video content, a hierarchical temporal association mining approach is developed to systematically capture the characteristic temporal patterns with respect to the events of interest. In this process, the unique challenges caused by the loose video structure and skewed data distribution issues are effectively tackled. In addition, an adaptive mechanism is proposed to determine the essential thresholds which are generally defined manually in the traditional association rule mining (ARM) approach. This framework thus largely relaxes the dependence on the domain knowledge and contributes to the ultimate goal of automatic video content analysis.",
author = "Min Chen and Chen, {Shu Ching} and Mei-Ling Shyu",
year = "2007",
month = "12",
day = "1",
doi = "10.1109/ICDEW.2007.4400983",
language = "English",
isbn = "1424408326",
pages = "137--145",
booktitle = "Proceedings - International Conference on Data Engineering",

}

TY - GEN

T1 - Hierarchical temporal association mining for video event detection in video databases

AU - Chen, Min

AU - Chen, Shu Ching

AU - Shyu, Mei-Ling

PY - 2007/12/1

Y1 - 2007/12/1

N2 - With the proliferation of multimedia data and ever-growing requests for multimedia applications, new challenges are emerged for efficient and effective managing and accessing large audio-visual collections. In this paper, we present a novel framework for video event detection, which plays an essential role in high-level video indexing and retrieval. Especially, since temporal information in a video sequence is critical in conveying video content, a hierarchical temporal association mining approach is developed to systematically capture the characteristic temporal patterns with respect to the events of interest. In this process, the unique challenges caused by the loose video structure and skewed data distribution issues are effectively tackled. In addition, an adaptive mechanism is proposed to determine the essential thresholds which are generally defined manually in the traditional association rule mining (ARM) approach. This framework thus largely relaxes the dependence on the domain knowledge and contributes to the ultimate goal of automatic video content analysis.

AB - With the proliferation of multimedia data and ever-growing requests for multimedia applications, new challenges are emerged for efficient and effective managing and accessing large audio-visual collections. In this paper, we present a novel framework for video event detection, which plays an essential role in high-level video indexing and retrieval. Especially, since temporal information in a video sequence is critical in conveying video content, a hierarchical temporal association mining approach is developed to systematically capture the characteristic temporal patterns with respect to the events of interest. In this process, the unique challenges caused by the loose video structure and skewed data distribution issues are effectively tackled. In addition, an adaptive mechanism is proposed to determine the essential thresholds which are generally defined manually in the traditional association rule mining (ARM) approach. This framework thus largely relaxes the dependence on the domain knowledge and contributes to the ultimate goal of automatic video content analysis.

UR - http://www.scopus.com/inward/record.url?scp=48349115410&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=48349115410&partnerID=8YFLogxK

U2 - 10.1109/ICDEW.2007.4400983

DO - 10.1109/ICDEW.2007.4400983

M3 - Conference contribution

SN - 1424408326

SN - 9781424408320

SP - 137

EP - 145

BT - Proceedings - International Conference on Data Engineering

ER -