tailieunhanh - Báo cáo hóa học: " Semantic Context Detection Using Audio Event Fusion: Camera-Ready Version"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Semantic Context Detection Using Audio Event Fusion: Camera-Ready Version | Hindawi Publishing Corporation EURASIP Journal on Applied Signal Processing Volume 2006 Article ID27390 Pages 1-12 DOI ASP 2006 27390 Semantic Context Detection Using Audio Event Fusion Camera-Ready Version Wei-Ta Chu 1 Wen-Huang Cheng 2 and Ja-Ling Wu1 2 1 Department of Computer Science and Information Engineering National Taiwan University Taipei 106 Taiwan 2 Graduate Institute of Networking and Multimedia National Taiwan University Taipei 106 Taiwan Received 31 August 2004 Revised 20 February 2005 Accepted 5 April 2005 Semantic-level content analysis is a crucial issue in achieving efficient content retrieval and management. We propose a hierarchical approach that models audio events over a time series in order to accomplish semantic context detection. Two levels of modeling audio event and semantic context modeling are devised to bridge the gap between physical audio features and semantic concepts. In this work hidden Markov models HMMs are used to model four representative audio events that is gunshot explosion engine and car braking in action movies. At the semantic context level generative ergodic hidden Markov model and discriminative support vector machine SVM approaches are investigated to fuse the characteristics and correlations among audio events which provide cues for detecting gunplay and car-chasing scenes. The experimental results demonstrate the effectiveness of the proposed approaches and provide a preliminary framework for information mining by using audio characteristics. Copyright 2006 Hindawi Publishing Corporation. All rights reserved. 1. INTRODUCTION As the rapid advance in media creation storage and compression technologies large amounts of multimedia content have been created and disseminated by various ways. Massive multimedia data challenge users in content browsing and retrieving thereby motivating the urging needs of information mining technologies. To facilitate effective or efficient multimedia document indexing many .