Video Data Mining: Semantic Indexing and Event Detection from the Association Perspective

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Author

Xingquan Zhu, Xindong Wu, Ahmed K. Elmagarmid, Zhe Feng, and Lide Wu

Tech report number

CERIAS TR 2005-116

Entry type

article

Abstract

Advances in the media and entertainment industries, including streaming audio and digital TV, present new challenges for managing and accessing large audio-visual collections. Current content management systems support retrieval using low-level features, such as motion, color, and texture. However, low-level features often have little meaning for naive users, who much prefer to identify content using high-level semantics or concepts. This creates a gap between systems and their users that must be bridged for these systems to be used effectively. To this end, in this paper, we first present a knowledge-based video indexing and content management framework for domain specific videos (using basketball video as an example). We will provide a solution to explore video knowledge by mining associations from video data. The explicit definitions and evaluation measures (e.g., temporal support and confidence) for video associations are proposed by integrating the distinct feature of video data. Our approach uses video processing techniques to find visual and audio cues (e.g., court field, camera motion activities, and applause), introduces multilevel sequential association mining to explore associations among the audio and visual cues, classifies the associations by assigning each of them with a class label, and uses their appearances in the video to construct video indices. Our experimental results demonstrate the performance of the proposed approach.

Date

2005 – 05

Booktitle

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,

Key alpha

Video mining, multimedia systems, database management, knowledge-based systems.

Number

Publisher

IEEE

Volume

Affiliation

IEEE

Publication Date

2005-05-01

Copyright

2005 IEEE

Keywords

Video mining, multimedia systems, database management, knowledge-based systems.

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@Article{ Video mining, multimedia systems, database management, knowledge-based systems. ,
	title = "Video Data Mining: Semantic Indexing and Event Detection from the Association Perspective",
	author = "Xingquan Zhu, Xindong Wu, Ahmed K. Elmagarmid, Zhe Feng, and Lide Wu",
	year = "2005",
	month = "05",
	booktitle = "IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, ",
	number = "05",
	publisher = "IEEE",
	volume = "17",
	abstract = "Advances in the media and entertainment industries, including streaming audio and digital TV, present new challenges for 
managing and accessing large audio-visual collections. Current content management systems support retrieval using low-level 
features, such as motion, color, and texture. However, low-level features often have little meaning for naive users, who much prefer to 
identify content using high-level semantics or concepts. This creates a gap between systems and their users that must be bridged for 
these systems to be used effectively. To this end, in this paper, we first present a knowledge-based video indexing and content 
management framework for domain specific videos (using basketball video as an example). We will provide a solution to explore video 
knowledge by mining associations from video data. The explicit definitions and evaluation measures (e.g., temporal support and 
confidence) for video associations are proposed by integrating the distinct feature of video data. Our approach uses video processing 
techniques to find visual and audio cues (e.g., court field, camera motion activities, and applause), introduces multilevel sequential 
association mining to explore associations among the audio and visual cues, classifies the associations by assigning each of them with 
a class label, and uses their appearances in the video to construct video indices. Our experimental results demonstrate the 
performance of the proposed approach. ",
	affiliation = "IEEE",
	copyright = "2005 IEEE",
	keywords = "Video mining, multimedia systems, database management, knowledge-based systems. ",
}