Graph is an important class of representations in pattern recognition. Graph-based representation and learning/inference algorithms have been widely applied to structural pattern recognition and image analysis, such as image segmentation, shape recognition, scene parsing, document analysis, social network mining, and so on. On the other hand, the application needs in network era have posed new challenges to graph-based pattern recognition, such as matching for large graphs, automatic learning of graph models, inference in graphical models incorporating multi-source knowledge and contexts, applications to practical large and noisy data, and so on.
Facing the multitude of scientific problems and the wide applications of graph-based representations, the IAPR TC-15 (Graph-based Representations in Pattern Recognition) has sponsored a series of workshop called IAPR-TC15 Workshop on Graph-based Representations in Pattern Recognition (GbR). The workshop has benefitted the community in triggering scientific research and exchanging progresses.
This special issue is aimed to report the state of the art in theory, methods and applications in graph-based pattern representation and recognition. The scope ranges from various computing issues of graph-based representation, learning and inference, to applications in pattern recognition, computer vision and data mining. The topics include, but are not limited to:
Graph feature extraction
Kernel methods for graphs
Graph-based classification and clusteringGraph-based image segmentation
Graph-based shape recognition
Graph-based machine learning
Data mining with graphs
Probabilistic graphical models
Applications in document analysis and other fields
Paper Submission due: November 15, 2015
Tentative first review notification: February 15, 2016
Tentative revision submission: April 28, 2016
Tentative acceptance notification: June 15, 2016
Papers must be submitted online in the period October 15, 2015 – November 15, 2015 via the Pattern Recognition Letters website, selecting the choice that indicates this special issue (identifier: GbPR).
Prepare your paper by carefully following the Journal guidelines for Authors, which include specifications for submissions aimed at Special Issues. In particular, you will find the templates for preparing the paper in the journal layout. Note that a maximum of 10 pages in the Journal layout is admitted for special issue papers.
Cheng-Lin Liu (Managing Guest Editor)
Institute of Automation of Chinese Academy of Sciences, Beijing, China
Anhui University, Hefei, China
Vienna University of Technology, Austria