The nature of statistical learning theory
The nature of statistical learning theory
Hierarchical Decomposition and Axial Shape Description
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Tree-Like Data Structures for Effective Recognition of 2-D Solids
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Multiresolution Spatial Partitioning for Shape Representation
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Moment-Based Pattern Representation Using Shape and Grayscale Features
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Pattern Recognition, Fourth Edition
Pattern Recognition, Fourth Edition
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A new approach is proposed to constructing a classifier of two-dimensional (2D) objects in a space of multiresolution object representations. The approach is based on constructing tree-structured covers (TSCs) of clusters of a training set by spheres in the space of the object representations taken at the maximum resolution level. The covering spheres and their projections of all resolution levels generate a multilevel network of templates in which the sphere centers yield the templates, while the spheres themselves form the influence regions of the templates at the corresponding resolution levels. Using the multilevel structure of the template network, a hierarchical search algorithm is proposed for making a decision group of the templates by a given voting criterion. A computational complexity of this algorithm is evaluated. An efficiency of the proposed TSC classifier is demonstrated by estimates of error rates in experiments on signature, hand gesture and face recognition, as well as by the comparative error rates obtained for these sources using the known SVM classifier.