Title :
Semantic event detection via multimodal data mining
Author :
Chen, Min ; Chen, Shu-Ching ; Shyu, Mei-Ling ; Wickramaratna, Kasun
Author_Institution :
Sch. of Comput. & Inc. Sci., Florida Int. Univ., Miami, FL
fDate :
3/1/2006 12:00:00 AM
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 :
data mining; feature extraction; video signal processing; event patterns; little human interference; low-level feature extraction; multimodal data mining; semantic event detection; soccer goal events; soccer video data; temporal analysis; temporal pattern analysis; video event detection; Data analysis; Data mining; Decision making; Event detection; Feature extraction; Hidden Markov models; Humans; Interference; Pattern analysis; Production;
Journal_Title :
Signal Processing Magazine, IEEE
DOI :
10.1109/MSP.2006.1621447