Abstract
The rapid expansion of the internet and the wide use of digital data have increased the need for both efficient image database creation and retrieval procedure. In this paper, texture classification based on the combination of texture features is proposed. Since most significant information of a texture often appears in the high frequency channels, the features are extracted by the computation of LBP and Texture Spectrum histogram. Euclidean distance is used for similarity measurement. The experimental result shows that 97.99% classification accuracy is obtained by the proposed method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Long, F., Zhang, H., Feng, D.D.: Fundamentals of Content Based Image Retrieval
Tamura, H., Mori, S., Yamawaki, T.: Textures Corresponding to Visual Perception. IEEE Trans. syst. Man Cybern, SMC 8(6), 460–473 (1978)
Rui, Y., Huang, T.S., Chang, S.-F.: Image Retrieval: Current Techniques, Promising Directions, and Open Issues. Journal of Visual Communication and Image Representation 10(1), 39–62 (1999)
Cross, G.R., Jain, A.K.: Markov random field texture models. IEEE Trans. Pattern Anal. Machine Intell. PAMI 5(1), 25–39 (1983)
Haralick, R.M., Shanmuga, K., Dinstein, I.: Textural Features for Image Classification. IEEE Transactions on Systems, Man and Cybernetics SMC 3, 610–621 (1973)
Chen, C.C.: Markov Random Fields in Image Analysis, Ph.D. Thesis, Computer Science Department, Michigan State University, East Lansing, MI (1988)
Rignot, E., Kwok, R.: Extraction of Textural Features in SAR Images: Statistical Model and Sensitivity. In: Proceedings of International Geoscience and Remote Sensing Symposium, Washington, DC, pp. 1979–1982 (1990)
Manjunath, B.S., Ma, W.Y.: Texture Features for Browsing and Retrieval of Image Data. IEEE Transactions on Pattern Analysis and Machine 18(8), 837–842 (1996)
Li, S.Z., Huang, X., Wang, Y.: Shape localization based on statistical method using extended local binary pattern. In: IEEE Proc. Conf. Image and Graphics, pp. 184–187 (2004)
Pass, G., Zabih, R.: Computer Science Department. Cornell University. Ithaca, Histogram Refinement for Content-Based Image Retrieval
Arivazhagan, S., Ganesan, L.: Texture classification using wavelet transform. Pattern Recognition Letters 24, 1513–1521 (2003)
Brodatz, P.: Textures: A Photographic Album for Artists and Designers. Dover, New York (1966)
Chang, T., Jay Kuo, C.C.: Texture analysis and classification with tree-structured wavelet transform. IEEE Trans. Image Process 2(4), 429–441 (1993)
Chellappa, R., Chatterjee, S.: Classification of texture using Gaussian Markov Random Fields. IEEE Trans. Acoustic Speech Signal Process, ASSP 33(4), 959–963 (1985)
He, D.C., Wang, L.: Texture unit, Texture spectrum and Texture Analysis. IEEE transactions on Geoscience and Remote Sensing 28(4) (July 1990)
He, D.C., Wang, L.: Texture classification using texture spectrum, vol. 23(8) (1990)
Jain, A.K.: Fundamentals of Digital image Processing. Prentice Hall, Englewood Cliffs (1989)
Hung, C.-C., Pham, M., Arasteh, S.: Image Texture Classification Using Texture Spectrum and Local Binary Pattern. IEEE transactions on pattern recognition, 2739–2742 (2006)
Zhitao, X., Minx, Y., Chengming, G.: Using Spectrum to Extract Texture Feature. IEEE transactions on image processing, 657–659 (2002)
Hiremath, P.S., Shivashankar, S.: Texture Classification using Wavelet Packet Decomposition. GVIP Journal 6(2) (September 2006)
Ojala, T., Pietikäinen, M., Mäenpää, 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)
Tuceryan, M., Jain, A.K.: Texture Analysis handbook of Pattern Recognition and Computer Vision (1994)
Jian, M., Guo, H., Liu, L.: Texture Image Classification Using Visual Perceptual Texture Features and Gabor Wavelet Features. Journal of Computers 4(8) (August 2009)
Borchani, M., Stamon, G.: Texture features for Image Classification and Retrieval. In: SPIE, vol. 3229, p. 401(1997) (March 2005)
Karkanis, S., Galousi, K., Maroulis, D.: Classification of Endoscopic Images Based on Texture Spectrum
Wiselin Jiji, G., Ganesan, L.: Unsupervised Segmentation using Fuzzy Logic based Texture Spectrum for MRI Brain Images. World Academy of Science, Engineering and Technology 5 (2005)
Ojala, T., Pietikäinen, M.: Unsupervised Texture Segmentation Using Feature Distributions. Pattern Recognition 32, 477–486, 1495-1501 (1999), http://www.cssip.elec.uq.edu.au/guy/meastex/meastex.html
Smith, J.R., Chang, S.-F.: Visually searching the web for content, IEEE Multimedia Magazine 4(3), 12–20 (1997) (Columbia U. CU/CTR Technical Report 459-96-25)
Smith, J.R., Chang, S.F.: Transform features for texture classification and discrimination in large image databases. In: Proc. IEEE Int. Conf. on Image Proc. (1994)
Thyagarajan, K.S., Nguyen, T., Persons, C.: A maximum likelihood approach to texture classification using wavelet transform. In: Proc. IEEE Int. Conf. on Image Proc. (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Vijayalakshmi, B., Subbiah Bharathi, V. (2011). A Hybrid Approach to Texture Classification. In: Wyld, D.C., Wozniak, M., Chaki, N., Meghanathan, N., Nagamalai, D. (eds) Advances in Network Security and Applications. CNSA 2011. Communications in Computer and Information Science, vol 196. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22540-6_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-22540-6_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22539-0
Online ISBN: 978-3-642-22540-6
eBook Packages: Computer ScienceComputer Science (R0)