JOURNAL ARTICLE

Semantic event detection via multimodal data mining

Min ChenShu‐Ching ChenMei‐Ling ShyuK. Wickramaratna

Year: 2006 Journal:   IEEE Signal Processing Magazine Vol: 23 (2)Pages: 38-46   Publisher: Institute of Electrical and Electronics Engineers

Abstract

This paper presents a novel framework for video event detection. The core of the framework is an advanced temporal analysis and multimodal data mining method that consists of three major components: low-level feature extraction, temporal pattern analysis, and multimodal data mining. One of the unique characteristics of this framework is that it offers strong generality and extensibility with the capability of exploring representative event patterns with little human interference. The framework is presented with its application to the detection of the soccer goal events over a large collection of soccer video data with various production styles.

Keywords:
Computer science Generality Event (particle physics) Feature extraction Data mining Extensibility Artificial intelligence Feature (linguistics)

Metrics

58
Cited By
5.44
FWCI (Field Weighted Citation Impact)
25
Refs
0.96
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Video Analysis and Summarization
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Music and Audio Processing
Physical Sciences →  Computer Science →  Signal Processing
Time Series Analysis and Forecasting
Physical Sciences →  Computer Science →  Signal Processing

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