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Automatic Wood Classification using a Novel Color Texture Features

by Shivashankar S., Madhuri R. Kagale
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 180 - Number 27
Year of Publication: 2018
Authors: Shivashankar S., Madhuri R. Kagale
10.5120/ijca2018916648

Shivashankar S., Madhuri R. Kagale . Automatic Wood Classification using a Novel Color Texture Features. International Journal of Computer Applications. 180, 27 ( Mar 2018), 34-38. DOI=10.5120/ijca2018916648

@article{ 10.5120/ijca2018916648,
author = { Shivashankar S., Madhuri R. Kagale },
title = { Automatic Wood Classification using a Novel Color Texture Features },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2018 },
volume = { 180 },
number = { 27 },
month = { Mar },
year = { 2018 },
issn = { 0975-8887 },
pages = { 34-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume180/number27/29147-2018916648/ },
doi = { 10.5120/ijca2018916648 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:01:58.625202+05:30
%A Shivashankar S.
%A Madhuri R. Kagale
%T Automatic Wood Classification using a Novel Color Texture Features
%J International Journal of Computer Applications
%@ 0975-8887
%V 180
%N 27
%P 34-38
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A variety of texture classification approaches have been reported in the literature but many of them are focused on gray-scale textures. The aim of this work is to develop a novel color texture features by constructing a histogram based on the combined intensity and color channel information to effectively classify color texture images. Five features are computed from the histogram bin values to reduce the computational complexity. Experiments are conducted on a set of 164 color texture images from VisTex database. The K-Nearest Neighbor (K-NN) classification method is used as a classifier. The classification results are encouraging to use the proposed scheme with reduction in features. Further the proposed scheme is used in automatic wood classification to show the usefulness of the proposed scheme in industrial applications.

References
  1. Barata, C., Ruela, M., Francisco, M., Mendonça, T., and Marques, J. S. 2014. Two systems for the detection of melanomas in dermoscopy images using texture and color features. IEEE Systems Journal, 8(3), 965-979.
  2. Sertel, O., Kong, J., Catalyurek, U. V., Lozanski, G., Saltz, J. H., and Gurcan, M. N. 2009. Histopathological image analysis using model-based intermediate representations and color texture: Follicular lymphoma grading. Journal of Signal Processing Systems, 55(1-3), 169.
  3. Hiremath, P. S., Shivashankar, S., and Pujari, J. 2006. Wavelet based features for color texture classification with application to CBIR. International Journal of Computer Science and Network Security, 6(9A), 124-133.
  4. Yadav, A. K., Roy, R., and Kumar, A. P. 2014. Survey on content based image retrieval and texture analysis with applications. International Journal of Signal Processing, Image Processing and Pattern Recognition, 7(6), 41-50.
  5. Kukkonen, S., Kailviaiinen, H., and Parkkinen, J. 2001. Color features for quality control in ceramic tile industry. Optical Engineering, 40(2), 170-177.
  6. Semnani, D., and Sheikhzadeh, M. 2009. New intelligent method of evaluating the regularity of weft-knitted fabrics by computer vision and grading development. Textile research journal, 79(17), 1578-1587.
  7. Yeh, C., and Perng, D. B. 2001. Establishing a demerit count reference standard for the classification and grading of leather hides. The International Journal of Advanced Manufacturing Technology, 18(10), 731-738.
  8. Maldonado, J. O., and Grana, M. 2009. Recycled paper visual indexing for quality control. Expert Systems with Applications, 36(5), 8807-8815.
  9. Sobey, P. J., and Semple, E. C. 1989. Detection and sizing visual features in wood using tonal measures and a classification algorithm. Pattern Recognition, 22(4), 367-380.
  10. Lycken, A. 2006. Comparison between automatic and manual quality grading of sawn softwood. Forest products journal, 56(4), 13.
  11. Piuri, V., and Scotti, F. 2010. Design of an automatic wood types classification system by using fluorescence spectra. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(3), 358-366.
  12. Bombardier, V., and Schmitt, E. 2010. Fuzzy rule classifier: Capability for generalization in wood color recognition. Engineering Applications of Artificial Intelligence, 23(6), 978-988.
  13. Akhloufi, M. A., Larbi, W. B., and Maldague, X. 2007. Framework for color-texture classification in machine vision inspection of industrial products. In Systems, Man and Cybernetics, 2007. ISIC. IEEE International Conference on (pp. 1067-1071). IEEE.
  14. Buchelt, B., and Wagenfuhr, A. 2012. Evaluation of colour differences on wood surfaces. European Journal of Wood and Wood Products, 70(1-3), 389-391.
  15. Kurdthongmee, W. 2008. Colour classification of rubberwood boards for fingerjoint manufacturing using a SOM neural network and image processing. Computers and electronics in agriculture, 64(2), 85-92.
  16. Shivashankar S., Kagale M. R., and Hiremath P. S. “Inter intensity and color channel co-occurrence histogram for color texture classification”, In Springer proceedings of the third international conference on Cognitive Computing and Information Processing (CCIP) , Dec 2017, Bengaluru, in press.
  17. Hiremath P. S., and Shivashankar S. 2008. Wavelet based co-occurrence histogram features for texture classification with an application to script identification in a document image. Pattern Recognition Letters 29, 1182-1189.
  18. Duda, R. O., Hart, P. E., and Stork, D. G. 2012. Pattern classification. John Wiley & Sons.
  19. VisTex, 1995. Vision Texture Database, vision and modeling group. MIT media laboratory. http://wwwwhite.media.mit.edu/vismod/imagery/VisionTexture/vistex.html
  20. Paschos, G., and Petrou, M. 2003. Histogram ratio features for color texture classification. Pattern Recognition Letters, 24(1), 309-314.
  21. Bianconi, F., Fernández, A., González, E., and Saetta, S. A. 2013. Performance analysis of colour descriptors for parquet sorting. Expert Systems with Applications, 40(5), 1636-1644.
  22. Parquet 2012. Parquet image dataset. Available online at http://dismac.dii.unipg.it/parquet/data.html
Index Terms

Computer Science
Information Sciences

Keywords

Intensity and color channel Histogram bins Feature computation Wood classification Color texture features K-NN classifier.

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