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: Research Article A Similarity-Based Approach for Audiovisual Document Classification Using Temporal Relation Analysis | Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2011 Article ID 537372 19 pages doi 2011 537372 Research Article A Similarity-Based Approach for Audiovisual Document Classification Using Temporal Relation Analysis Zein Al Abidin Ibrahim 1 Isabelle Ferrane 2 and Philippe Joly2 1 LERIA Laboratory Angers University 49045 Angers France 2IRIT Laboratory Toulouse University 31062 Toulouse France Correspondence should be addressed to Zein Al Abidin Ibrahim zibrahim@ Received 1 June 2010 Revised 28 January 2011 Accepted 1March2011 Academic Editor Sid-Ahmed Berrani Copyright 2011 Zein Al Abidin Ibrahim et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. We propose a novel approach for video classification that bases on the analysis of the temporal relationships between the basic events in audiovisual documents. Starting from basic segmentation results we define a new representation method that is called Temporal Relation Matrix TRM . Each document is then described by a set of TRMs the analysis of which makes events of a higher level stand out. This representation has been first designed to analyze any audiovisual document in order to find events that may well characterize its content and its structure. The aim of this work is to use this representation to compute a similarity measure between two documents. Approaches for audiovisual documents classification are presented and discussed. Experimentations are done on a set of 242 video documents and the results show the efficiency of our proposals. 1. Introduction Motivated by the fact that large scale document indexing cannot be handled by human operators researches tend to use high-level automatic indexing with the recent existing huge masses of digital data. Several automatic tools are based on .
