Cascaded and Hierarchical Neural Networks for Classifying Surface Images of Marble Slabs

SELVER M. A., AKAY O., Ardali E., YAVUZ A. B., ÖNAL O., Oezden G.

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS, cilt.39, sa.4, ss.426-439, 2009 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 39 Sayı: 4
  • Basım Tarihi: 2009
  • Doi Numarası: 10.1109/tsmcc.2009.2013816
  • Dergi Adı: IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.426-439
  • Anahtar Kelimeler: Artificial neural networks, classification of surface images of marble slabs, feature extraction, hierarchical radial basis function networks, AUTOMATIC SYSTEM, CLASSIFICATION, INSPECTION, SEGMENTATION, CLASSIFIERS, HISTOGRAMS, TEXTURES
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

Marble quality classification is an important procedure generally performed by human experts. However, using human experts for classification is error prone and subjective. Therefore, automatic and computerized methods are needed in order to obtain reproducible and objective results. Although several methods are proposed for this purpose, we demonstrate that their performance is limited when dealing with diverse datasets containing a large number of quality groups. In this work, we test several feature sets and neural network topologies to obtain a better classification performance. During these tests, it is observed that different feature sets represent different subgroup(s) in a quality group rather than representing the whole group. Therefore, our approach is to use these features in a cascaded manner in which a quality group is classified by classifying all of its subgroups. We first realize this approach by using a two-stage cascaded network. Then, we design a hierarchical radial basis function network (HRBFN) in which correctly classified marble samples are taken out of the dataset and a different feature extraction method is applied to the remaining samples at each network level. The HRBFN system produces successful results for industrial applications and facilitates the desirable property of implementation in a quasi real-time manner.