| Date: |
2014 |
| Abstract: |
This thesis addresses the problem of lack of efficient computational tools for graph based structural pattern recognition approaches and proposes to exploit computational strength of statistical pattern recognition. It has two fold contributions. The first contribution is a new method of explicit graph embedding. The proposed graph embedding method exploits multilevel analysis of graph for extracting graph level information, structural level information and elementary level information from graphs. It embeds this information into a numeric feature vector. The method employs fuzzy overlapping trapezoidal intervals for addressing the noise sensitivity of graph representations and for minimizing the information loss while mapping from continuous graph space to discrete vector space. The method has unsupervised learning abilities and is capable of automatically adapting its parameters to underlying graph dataset. The second contribution is a framework for automatic indexing of graph repositories for graph retrieval and subgraph spotting. This framework exploits explicit graph embedding for representing the cliques of order 2 by numeric feature vectors, together with classification and clustering tools for automatically indexing a graph repository. It does not require a labeled learning set and can be easily deployed to a range of application domains, offering ease of query by example (QBE) and granularity of focused retrieval. |
| Note: |
Advisors: Jean-Yves Ramel, Josep Lladós and Thierry Brouard Date and location of PhD thesis defense: 2nd of March 2012 at University of Tours in France. |
| Rights: |
Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.  |
| Language: |
Anglès |
| Document: |
Altres ; recerca ; Versió publicada |
| Subject: |
Pattern recognition ;
Graph clustering ;
Graph classification ;
Graph embedding ;
Subgraph spotting ;
Fuzzy logic ;
Graphics recognition |
| Published in: |
ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 13, Núm. 2 (2014) , p. 7-8, ISSN 1577-5097 |
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