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|Title:||Hierarchical rule-based neural network for multi-object classification using invariant features||Authors:||İmamoğlu, N.
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.||URI:||https://doi.org/10.1109/INISTA.2011.5946079
|Appears in Collections:||Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering|
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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