GbRPR 2019 : International Workshop on Graph-Based Representations in Pattern Recognition

in Conferences   Posted on November 5, 2020 

Conference Information

Submission Deadline Saturday 19 Jan 2019 Proceedings indexed by :
Conference Dates Jun 19, 2019 - Jun 21, 2019
Conference Address Tours, France
Conference & Submission Link
Conference Organizers : ( Deadline extended ? Click here to edit )

Conference Ranking & Metrics (This is a TOP Conference)

Impact Score 1.49
#Contributing Top Scientists 4
#Papers published by Top Scientists 14
Google Scholar H5-index 0
Guide2Research Overall Ranking: 701
Category Rankings
Computational Theory and Mathematics 62
Databases & Information Systems 117
Machine Learning & Artificial Intelligence 151

Conference Call for Papers

GbR is a biennial workshop organized by the 15th Technical Committee of IAPR, aimed at encouraging research works in Pattern Recognition and Image Analysis within the graph theory framework. This workshop series traditionally provides a forum for presenting and discussing research results and applications in the intersection of pattern recognition, image analysis on one side and graph theory on the other side. In addition, given the avenue of new structural/graphical models and structural criteria for solving computer vision problems, GbR2019 organization encourages researchers in this more general context to actively participate in the workshop. Furthermore, the application of graphs to pattern recognition problems in other fields like computational topology, graphic recognition systems and bioinformatics is also highly welcome at the workshop.

The scope of GbR2019 includes, but is not limited to, the following topics:

Graph matching
Graph-based image segmentation
Deep Learning on graphs
Graph representation of shapes
Graph-based learning and clustering
Data mining with graphs
Graph distance and similarity measures
Kernel methods for graphs
Graph embeddings
Belief-propagation methods
Graph-cuts methods
Graphs in computational topology and bioinformatics
Graphs in social network analysis

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