Recognizing Handwritten Digits Using Hierarchical Products of Experts
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Theoretical and Experimental Analysis of a Two-Stage System for Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
CAIP '97 Proceedings of the 7th International Conference on Computer Analysis of Images and Patterns
Traffic Sign Recognition Revisited
Mustererkennung 1999, 21. DAGM-Symposium
Recognition of Handwritten Numerals Using Gabor Features
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
An Incremental and Hierarchical K-NN Classifier for Handwritten Characters
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Classification Using a Hierarchical Bayesian Approach
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
A Bayesian network classifier and hierarchical Gabor features for handwritten numeral recognition
Pattern Recognition Letters
Grid-Clustering: An Efficient Hierarchical Clustering Method for Very Large Data Sets
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Script identification from indian documents
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
Using self-organising maps in the detection and recognition of road signs
Image and Vision Computing
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In recent years it has been shown that hierarchical classifiers have a significant advantage over single stage classifiers both in classification accuracy and in complexity of the classification features. This paper introduces a new method for creating the structure of hierarchical classifiers using a novel method for determining clusters. The proposed method uses features obtained using Gabor wavelets to create similarity maps, which help separating the class space into smaller more distinctive clusters. This approach has been applied on the Road Sign Recognition problem and has shown encouraging results in comparison to k-means algorithm.