Efficient Marble Slab Classification using Simple Features

Mert Kilickaya, Umut Cinar, Sinan Ugurluoglu

2016

Abstract

The marbles consist a large part of the buildings and widely used. Though, the manufacturing process for marbles are time consuming and inefficient: Human experts assign inconsistent labels to different marble classes causing a big loss of time and money. It arises the need for an automatic method of classifying marbles. In this paper we present a novel method which utilizes color, structural and textural representations of a marble. Once the representation is combined with an accurate segmentation step, it achieves an accuracy of 94% on a newly collected dataset of 1000 images. We suggest the best settings for an automatic marble classification system which is simple and fast enough to be used in a real-life environment like marble factories.

References

  1. Achanta, R., Hemami, S., Estrada, F., and Susstrunk, S. (2009). Frequency-tuned salient region detection. In Computer vision and pattern recognition, 2009. cvpr 2009. ieee conference on, pages 1597-1604. IEEE.
  2. Ar, I. and Akgul, Y. S. (2008). A generic system for the classification of marble tiles using gabor filters. In Computer and Information Sciences, 2008. ISCIS'08. 23rd International Symposium on, pages 1-6. IEEE.
  3. Arivazhagan, S., Ganesan, L., and Angayarkanni, V. (2005). Color texture classification using wavelet transform. In Computational Intelligence and Multimedia Applications, 2005. Sixth International Conference on, pages 315-320. IEEE.
  4. Bell, S., Upchurch, P., Snavely, N., and Bala, K. (2014). Material recognition in the wild with the materials in context database. arXiv preprint arXiv:1412.0623.
  5. Bianconi, F., González, E., Fernández, A., and Saetta, S. A. (2012). Automatic classification of granite tiles through colour and texture features. Expert Systems with Applications, 39(12):11212-11218.
  6. Cheng, M., Mitra, N. J., Huang, X., Torr, P. H., and Hu, S. (2015). Global contrast based salient region detection. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 37(3):569-582.
  7. Cimpoi, M., Maji, S., and Vedaldi, A. (2014). Deep convolutional filter banks for texture recognition and segmentation. arXiv preprint arXiv:1411.6836.
  8. C¸inar, U., Karaman, E., Gedik, E., Yardimci, Y., and Halici, U. (2012). A new approach to automatic road extraction from satellite images using boosted classifiers. In SPIE Remote Sensing, pages 85370O-85370O. International Society for Optics and Photonics.
  9. Jolliffe, I. (2002). Principal component analysis. Wiley Online Library.
  10. Lam, S. W.-C. (1996). Texture feature extraction using gray level gradient based co-occurence matrices. In Systems, Man, and Cybernetics, 1996., IEEE International Conference on, volume 1, pages 267-271. IEEE.
  11. Leung, T. and Malik, J. (2001). Representing and recognizing the visual appearance of materials using threedimensional textons. International journal of computer vision, 43(1):29-44.
  12. Martínez-Alajar ín, J., Luis-Delgado, J. D., and Tom ásBalibrea, L. M. (2005). Automatic system for qualitybased classification of marble textures. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 35(4):488-497.
  13. Otsu, N. (1975). A threshold selection method from graylevel histograms. Automatica, 11(285-296):23-27.
  14. Perazzi, F., Krähenbühl, P., Pritch, Y., and Hornung, A. (2012). Saliency filters: Contrast based filtering for salient region detection. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 733-740. IEEE.
  15. Schmid, C. (2001). Constructing models for content-based image retrieval. In Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on, volume 2, pages II-39. IEEE.
Download


Paper Citation


in Harvard Style

Kilickaya M., Cinar U. and Ugurluoglu S. (2016). Efficient Marble Slab Classification using Simple Features . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 192-199. DOI: 10.5220/0005723201920199


in Bibtex Style

@conference{visapp16,
author={Mert Kilickaya and Umut Cinar and Sinan Ugurluoglu},
title={Efficient Marble Slab Classification using Simple Features},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={192-199},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005723201920199},
isbn={978-989-758-175-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)
TI - Efficient Marble Slab Classification using Simple Features
SN - 978-989-758-175-5
AU - Kilickaya M.
AU - Cinar U.
AU - Ugurluoglu S.
PY - 2016
SP - 192
EP - 199
DO - 10.5220/0005723201920199