Abstract
The analysis of texture in images is an important area of study. Image benchmarks such as Meastex and Vistex have been developed for researchers to compare their experiments on these texture benchmarks. In this paper we compare five different texture analysis methods on these benchmarks in terms of their recognition ability. Since these benchmarks are limited in terms of their content, we have divided each image into n images and performed our analysis on a larger data set. In this paper we investigate how well the following texture extraction methods perform: autocorrelation, co-occurrence matrices, edge frequency, Law’s, and primitive length. We aim to determine if some of these methods outperform others by a significant margin and whether by combining them into a single feature set will have a significant impact on the overall recognition performance. For our analysis we have used the linear and nearest neighbour classifiers.
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
J. M. H. Buf, M. Kardan and M. Spann, Texture feature performance for image segmentation, Pattern Recognition, 23(3/4):291–309, 1990.
R. W. Conners and C. A. Harlow, A theoretical comparison of texture algorithms, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2(3):204–222, 1980.
J. F. Haddon, J. F. Boyce, Co-occurrence matrices for image analysis, IEE Electronics and Communications Engineering Journal, 5(2):71–83, 1993.
R. M. Haralick, K. Shanmugam and I. Dinstein, Textural features for image classification, IEEE Transactions on System, Man, Cybernetics, 3:610–621, 1973.
K. Karu, A. K. Jain and R. M. Bolle, Is there any texture in the image? Pattern Recognition, 29(9):1437–1446, 1996.
K. I. Laws, Textured image segmentation, PhD Thesis, University of Southern California, Electrical Engineering, January 1980.
P. P. Ohanian and R. C. Dubes, Performance evaluation for four class of texture features, Pattern Recognition, 25(8):819–833, 1992.
T. Ojala, M. Pietikainen, A comparative study of texture measures with classification based on feature distributions, Pattern Recognition, 29(1):51–59, 1996.
O. Pichler, A. Teuner and B. J. Hosticka, A comparison of texture feature extraction using adaptive Gabor filter, pyramidal and tree structured wavelet transforms, Pattern Recognition, 29(5): 733–742, 1996.
W. K. Pratt, Digital image processing, John Wiley, New York, 1991.
T. Randen and J. H. Husθy, Filtering for texture classification: A comparative study, IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(4):291–310, 1999.
T. R. Reed and J. M. H. Buf, A review of recent texture segmentation and feature extraction techniques, Computer Vision, Image Processing and Graphics, 57(3):359–372, 1993.
J. Strand and T. Taxt, Local frequency features for texture classification, Pattern Recognition, 27(10):1397–1406, 1994.
G. Smith and I. Burns, Measuring texture classification algorithms, Pattern Recognition Letters, 18:1495–1501, 1997.
M. Sonka, V. Hlavac and R. Boyle, Image processing, analysis and machine vision, PWS publishing, San Francisco, 1999.
M. Tuceyran and A. K. Jain, Texture analysis, in Handbook of Pattern Recognition and Computer Vision,edC. H. Chen, L. F. Pau and P. S. P. Wang (Eds.), chapter 2, 235–276, World Scientific, Singapore, 1993.
L. vanGool, P. Dewaele and A. Oosterlinck, Texture analysis, Computer Vision, Graphics and Image Processing, 29:336–357, 1985.
J. S. Weszka, C. R. Dyer and A. Rosenfeld, A comparative study of texture measures for terrain classification, IEEE Transactions on Systems, Man and Cybernetics, 6:269–285, 1976.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Singh, S., Sharma, M. (2001). Texture Analysis Experiments with Meastex and Vistex Benchmarks. In: Singh, S., Murshed, N., Kropatsch, W. (eds) Advances in Pattern Recognition — ICAPR 2001. ICAPR 2001. Lecture Notes in Computer Science, vol 2013. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44732-6_43
Download citation
DOI: https://doi.org/10.1007/3-540-44732-6_43
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41767-5
Online ISBN: 978-3-540-44732-0
eBook Packages: Springer Book Archive