Virtual Special Issue on “Recent Advances in Statistical, Structural and Syntactic Pattern Recognition”
摘要截稿:
全文截稿: 2019-01-31
影响因子: 3.255
期刊难度:
CCF分类: C类
中科院JCR分区:
• 大类 : 计算机科学 - 3区
• 小类 : 计算机:人工智能 - 3区
Overview
Statistical, structural and syntactic pattern recognition are the most classical and important branches of pattern recognition research. They provide the fundamental theory and methods for a number of research areas in pattern detection, machine learning, computer vision, and data mining, leading to successful applications in many tasks such as handwriting and face recognition, image classification, video processing, and so on.
The study of statistical pattern recognition covers all stages of an investigation from problem formulation and data collection through to discrimination and classification, assessment of results and interpretation. It normally assumes that object samples are represented as feature vectors of fixed dimensionality, to which statistical techniques are applied for analyzing pattern variations. On the other hand, the study of syntactic & structural pattern recognition represents an object by a variable-cardinality set of symbolic, nominal features. This allows for representing pattern structures, taking into account more complex interrelationships between attributes than is possible in the case of numerical feature vectors of fixed dimensionality. As a result, the studies of statistical pattern recognition and syntactic & structural pattern recognition complement each other and make a comprehensive characterization of various pattern recognition problems.
Facing the fast development and wide applications in these areas, since 1996, the IAPR TC-1 (Statistical Pattern Recognition Techniques) and TC-2 (Structural and Syntactical Pattern Recognition) have sponsored a series of IAPR joint International Workshops on Statistical Techniques in Pattern Recognition and Structural and Syntactic Pattern Recognition (S+SSPR). S+SSPR is an international forum for exchanging ideas and discovering fancies in the fields of pattern recognition.
The proposed special issue will accept contributions significantly extended from papers presented in S+SSPR 2018, which is going to be held in Beijing during Aug. 17-19, 2018. It will also be a thematic issue open to the whole pattern recognition community. The scope of this special issue will be the same as S+SSPR conference, which ranges from structural based pattern recognition, statistical pattern recognition, and graphical model, to applications of deep learning, computer vision and data mining. The topics include, but are not limited to:
• Structural Matching
• Statistical Classification and Prediction
• Syntactic Pattern Recognition
• Graph-theoretic Methods
• Ensemble Methods
• Graphical Models
• Metric Learning
• Subspace Learning
• Structural Text Analysis
• Stochastic Structural Models
• Applications in statistical, structural and syntactic based pattern recognition methods