Please use this identifier to cite or link to this item:
Title: Hierarchical rule-based neural network for multi-object classification using invariant features
Authors: İmamoğlu, N.
Eresen, A.
Özbayoğlu, A. M.
Keywords: hierarchical rule-based neural networks
nearest neighbor and Bayesian classifiers
pseudo zernike moments
Issue Date: 2011
Source: 2011 International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2011, 15 June 2011 through 18 June 2011, Istanbul-Kadikoy, 85879
Abstract: Feature extraction techniques play a vital part in pattern recognition applications. In order to achieve the best performance in a particular classification problem, the most appropriate feature extractor for the problem is pursued. In this paper, a Pseudo-Zernike Moments based model is used as the feature extractor due to its reliability in illumination and rotation invariant multi-class object classification. A Hierarchical Rule-Based Neural Networks (HRB-NN) approach is proposed to classify multi-class data using hierarchical classification based on similarity measures between different classes. HRB-NN performance is compared to Nearest Neighbor and Bayesian classifiers. For implementation, a database of 960 images (640 training, 320 testing) for 8 different objects is used. The proposed method was able to classify the given data without any failure by giving the best performance outperforming the other chosen classifiers. © 2011 IEEE.
ISBN: 9781612849195
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Show full item record

CORE Recommender


checked on Sep 23, 2022

Page view(s)

checked on Dec 26, 2022

Google ScholarTM



Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.