Special Issue on Advances in Graph-based Representations for Pattern Recognition (AGbR4PR)
• 大类 : 工程技术 - 3区
• 小类 : 计算机：人工智能 - 3区
Graph-based representation and learning/inference algorithms are widely applied to structural pattern recognition, image analysis, machine learning and computer vision. Facing the multitude of scientific problems and the wide applications of graph-based representations, the IAPR TC-15 (Graph-based Representations in Pattern Recognition) promotes a series of workshops called IAPR-TC15 Workshop on Graph-based Representations in Pattern Recognition (GbR) since more than 20 years. This series of workshops has benefitted the community in triggering scientific research and exchanging progresses all along years. The 12th edition of GbR was held in Tours, France, in June 2019, and saw several original contributions linked to the actual strong interest for deep learning and artificial intelligence.
This special issue should aim to report the last advances in theory, methods and applications using graphs for pattern representation and recognition. The scope ranges from various computing issues like combining machine learning with graphs, graph mining, graph representations of shapes, images and networks, to applications in pattern recognition, computer vision and data mining.
The topics of the Special Issue include, but are not limited to:
Graph-based image segmentation
Machine Learning / 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
Graphs in computational topology and bioinformatics