Dong-Qing Zhang, Shih-Fu Chang. Detecting Image Near-Duplicate by Stochastic Attributed Relational Graph Matching with Learning. In ACM Multimedia, New York City, USA, October 2004.
Download paper:
Adobe portable document (pdf) 
Copyright notice:This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.
Detecting Image Near-Duplicate (IND) is an important problem in a variety of applications, such as copyright infringement detection and multimedia linking. Traditional image similarity models are often difficult to identify IND due to their inability to capture scene composition and semantics. We present a part-based image similarity measure derived from stochastic matching of Attribute Relational Graphs that represent the compositional parts and part relations of image scenes. Such a similarity model is fundamentally different from traditional approaches using low-level features or image alignment. The advantage of this model is its ability to accommodate spatial attribute relations and support supervised and unsupervised learning from training data. The experiments compare the presented model with several prior similarity models, such as color histogram, local edge descriptor, etc. The presented model outperforms the prior approaches with large margin
@InProceedings{duplicate_detect,
Author = {Zhang, Dong-Qing and Chang, Shih-Fu},
Title = {Detecting Image Near-Duplicate by Stochastic Attributed Relational Graph Matching with Learning},
BookTitle = {ACM Multimedia},
Address = {New York City, USA},
Month = {October},
Year = {2004}
}
Get EndNote Reference (.ref)
For problems or questions regarding this web site contact The
Web Master.
This document was translated automatically from BibTEX by bib2html (Copyright 2003 © Eric Marchand, INRIA, Vista Project).
