Video data mining: Semantic indexing and event detection from the association perspective

Xingquan Zhu, Xindong Wu, Ahmed Elmagarmid, Zhe Feng, Lide Wu

Research output: Contribution to journalArticle

100 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)665-677
Number of pages13
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number5
Publication statusPublished - 1 May 2005
Externally publishedYes



  • Database management
  • Knowledge-based systems
  • Multimedia systems
  • Video mining

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Information Systems

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