Abstract
This paper presents and evaluates a pixel-based texture classifier that integrates multiple texture feature extraction methods through a new scheme based on the Kullback J-divergence. Experimental results show that the proposed technique yields qualitatively better image segmentations than well-known both supervised and unsupervised texture classifiers based on specific families of texture methods. A practical application to fabric defect detection is presented.
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.
Similar content being viewed by others
References
Berger, J.: Statistical Decision Theory and Bayesian Analysis. Springer, Heidelberg (1985)
Brodatz, P.: Textures: A Photographic Albumfor Artists and Designers. Dover & Greer Publishing Company (1999)
Chang, K.I., Bowyer, K.W., Sivagurunath, M.: Evaluation of Texture Segmentation Algorithms. In: Proc. IEEECVPR, Fort Collins, USA (1999)
Deng, Y., Manjunath, B.S.: Unsupervised Segmentation of Color-Texture Regions in Images and Video. IEEE Trans. PAMI 23(8), 800–813 (2001)
Garcia, M.A., Puig, D.: Improving Texture Pattern Recognition by Integration of Multiple Texture Feature Extraction Methods. In: 16th IAPR Int. Conf. on Pattern Recognition, Quebec, Canada, vol. 3, pp. 7–10 (2002)
Kittler, J.: Feature Selection and Extraction. In: Young, T.Y., Fu, K.S. (eds.) Handbook of Pattern Recog. and Image Proc., pp. 60–81. Academic Press, London (1986)
Ma, W.Y., Manjunath, B.S.: Edge Flow: A Framework of Boundary Detection and Image Segmentation. In: IEEEInt. Conf. CVPR, Puerto Rico (1997)
Malik, J., Belongie, S., Leung, T., Shi, J.: Contour and Texture Analysis for Image Segmentation. In: Boyer, K.L., Sarkar, S. (eds.) Perceptual Organization for Artificial Vision Systems, Kluwer Academic Publishers, Dordrecht (2000)
Mathiassen, J.R., Skavhaug, A., Bo, K.: Texture Similarity Measure Using Kullback-Leibler Divergencebetween Gamma Distributions. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 133–147. Springer, Heidelberg (2002)
Puig, D., Garcia, M.A.: Recognizing Specific Texture Patterns by Integration of Multiple Texture Methods. In: IEEE ICIP, Rochester, USA, vol. 1, pp. 125–128 (2002)
Puzicha, J., Hofmann, T., Buhmann, J.M.: Non-parametric Similarity Measures for Unsupervised Texture Segmentation and Image Retrieval. In: IEEE Int. Conf. on Computer Vision and Pattern Recognition, Puerto Rico, pp. 267–272 (1997)
Randen, T., Husoy, J.H.: Filtering for Texture Classification: A Comparative Study. IEEE Trans. PAMI 21(4), 291–310 (1999)
Reed, T.R., Hans du Buf, J.M.: A Review of Recent Texture Segmentation and Feature Extraction Techniques. CVGIP: Image Underst. 57(3), 359–372 (1993)
Smith, G., Burns, I.: Measuring Texture Classification Algorithms. Pattern Recognition Letters 18, 1495–1501 (1997); MeasTex Image Texture Database and Test Suite. Centre for Sensor Signal and Information Proc., Universityof Queensland, Australia, http://www.cssip.uq.edu.au/staff/meastex/meastex.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Garcia, M.A., Puig, D. (2003). Pixel Classification by Divergence-Based Integration of Multiple Texture Methods and Its Application to Fabric Defect Detection. In: Michaelis, B., Krell, G. (eds) Pattern Recognition. DAGM 2003. Lecture Notes in Computer Science, vol 2781. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45243-0_18
Download citation
DOI: https://doi.org/10.1007/978-3-540-45243-0_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40861-1
Online ISBN: 978-3-540-45243-0
eBook Packages: Springer Book Archive