Editorial Board
Preface of Special Issue on “Graph-based Processing for Pattern Recognition”
A long trip in the charming world of graphs for Pattern Recognition
On the complexity of submap isomorphism and maximum common submap problems
Efficient subgraph matching using topological node feature constraints
Approximation of graph edit distance based on Hausdorff matching
A quantum Jensen–Shannon graph kernel for unattributed graphs
Treelet kernel incorporating cyclic, stereo and inter pattern information in chemoinformatics
Graph-based point drift: Graph centrality on the registration of point-sets
Exact solution to median surface problem using 3D graph search and application to parameter space exploration
An entropy-based persistence barcode
ECDS: An effective shape signature using electrical charge distribution on the shape
Randomized circle detection with isophotes curvature analysis
Determining shape and motion from monocular camera: A direct approach using normal flows
Unsupervised feature selection by regularized self-representation
Effective texture classification by texton encoding induced statistical features
Secure biometric template generation for multi-factor authentication
Noisy and incomplete fingerprint classification using local ridge distribution models
Fully automatic segmentation of breast ultrasound images based on breast characteristics in space and frequency domains
Learning descriptive visual representation for image classification and annotation
Fast computation of separable two-dimensional discrete invariant moments for image classification
Video summarization via minimum sparse reconstruction
A Dempster–Shafer Theory based combination of handwriting recognition systems with multiple rejection strategies
Efficient segmentation-free keyword spotting in historical document collections
Accurate 3D action recognition using learning on the Grassmann manifold
Trajectory-based human action segmentation
Multi-target tracking by learning local-to-global trajectory models
Quantification-oriented learning based on reliable classifiers
Noise-robust semi-supervised learning via fast sparse coding
