Paper Info
Title
Clustering Of Video Objects By Graph Matching
Abstract
We propose a new graph-based data structure, called Spatio temporal Region Graph (STRG) which can represent the content of video sequence. Unlike existing ones which consider mainly spatial information in the frame level of video, the proposed STRG is able to formulate its temporal information in the video level additionally. After an STRG is constructed from a given video sequence, it is decomposed into its subgraphs called Object Graphs (OGs), which represent the temporal characteristics of video objects. For unsupervised learning, we cluster similar OGs into a group, in which we need to match two OGs. For this graph matching, we introduce a new distance measure, called E.,tended Graph Edit Distance (ECED), which can handle the temporal characteristics of OGs. For actual clustering, we exploit Expectations Maximization (EM) with EGED. The experiments have been conducted on real video streams, and their results show the effectiveness and robustness of the proposed schemes.
Year
DOI
Venue
2005
10.1109/ICME.2005.1521443
2005 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME), VOLS 1 AND 2
Keywords
Field
DocType
clustering algorithms,edit distance,computer science,data structure,graph theory,robustness,data structures,data engineering,image processing,graph matching,clustering,expectation maximization,unsupervised learning
Graph theory,Data structure,Pattern recognition,Object graph,Computer science,Image processing,Matching (graph theory),Theoretical computer science,Unsupervised learning,Information engineering,Artificial intelligence,Cluster analysis
Conference
Citations 
PageRank 
References 
7
0.51
5
Authors
3
Name
Order
Citations
PageRank
Jeongkyu Lee128524.82
Oh Jung-hwan2252.97
Sae Hwang324717.88