Abstract
The e LBPs extract local structure information by establishing a relationship between the central pixel and its adjacent pixels. However, it is very sensitive to the change of the central pixel .In this paper,we choose a circle with the radius of 1 instead of a single center. The method is proposed for texture classification by comparing the information between the neighbors and the new center pixel. In order to decrease the feature size and increase the classification accuracy, both LBC-like feature and CLBP feature were used in the proposed method .Experiments are carried out on Outex and UIUC databases. The experimental results demonstrate that this method perform effectively.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)
Tan, X.Y., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Transactions on Image Processing 19(6), 1635–1650 (2010)
Guo, Z.H., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Transactions on Image Processing 19(6), 1657–1663 (2010)
Zhao, Y., Huang, D.S., Jia, W.: Completed Local Binary Count for rotation invariant texture classification. IEEE transaction on Image Processing 21, 4492–4497 (2012)
Zhao, G., Pietikäinen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 915–928 (2007)
http://www.cfar.umd.edu/~fer/High-resolution-data-base/hr_database.htm
Ojala, T., Maenpaa, T., Pietikainen, M., Viertola, J., Kyllönen, J., Huovinen, S.: Outex - new framework for empirical evaluation of texture analysis algorithms. In: Proc. 16th Int. Conf. Pattern Recognit., vol. 1, pp. 701–706 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Wei, H., Zhu, HD., Gan, Y., Shang, L. (2014). A New Local Binary Pattern in Texture Classification. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theory. ICIC 2014. Lecture Notes in Computer Science, vol 8588. Springer, Cham. https://doi.org/10.1007/978-3-319-09333-8_76
Download citation
DOI: https://doi.org/10.1007/978-3-319-09333-8_76
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09332-1
Online ISBN: 978-3-319-09333-8
eBook Packages: Computer ScienceComputer Science (R0)