Abstract
The RGB colour space is prominent as a colour representation and display scheme, although a number of other colour spaces have been developed over the years each with its own advantages and shortcomings with regard to its usefulness for colour/texture recognition. However, the recent advent of multiple classifier systems provides the unique opportunity to exploit the diverse information encapsulated in the different colour representations in a systematic fashion. In this paper we propose the use of classifier combination schemes which utilise information from different colour domains. We subsequently use suitable measures to investigate the diversity of the information infused by the different colour spaces. Experiments with two 40-class colour/texture datasets show the benefit of our multiple classifier approach, and reveal the existence of strong correlations between the accuracy achieved and the diversity measures. Finally, we illustrate, using quadratic regression, that there is significant scope to build and explore further (potentially causal) models of the observed relations between ensemble performance and diversity metrics. Our results point towards the use of diversity along with other statistical measures as possible predictors of the ensemble behaviour.
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
Garcia, M.A., Puig, D.: Improving Texture Pattern Recognition by Integration of Multiple Texture Feature Extraction Methods. In: Proc. 16 th International Conference on Pattern Recognition (ICPR 2002), Quebec City, QC, Canada, vol. 3 (2002)
Singh, M., Markou, M., Singh, S.: Colour Image Texture Analysis: Dependence on Colour Spaces. In: Proc. 15th Int. Conf. on Pat. Recog (ICPR 2002), Canada (2002)
Chindaro, S., Deravi, F.: Directional Properties of Colour Co-occurrence Matrices for Lip Location and Segmentation, August 17. LNCS, pp. 84–85 (2001)
Kuncheva, L.I., Whitaker, C.J.: Measures of diversity in classifier ensembles. Machine Learning 51, 181–207 (2003)
Tumer, K., Ghosh, J.: Error correlation and reduction in ensemble classifiers. In: Connection Science, vol. 8(3/4), pp. 127–161. Springer, London (1996)
Cunningham, P., Carney, J.: Diversity versus quality in classification ensembles based on feature selection. Technical Report TCD-CS-200-02, Department of Computer Science, Trinity College Dublin (2000)
Patridge, D., Krazanowski, W.J.: Software Diversity: Practical Statistics for its measurement and exploitation. Inf. and Software Techn. 39, 707–717 (1997)
Foely, J., Van Dam, A., Feiner, S., Hughes, J.: Computer Graphics. Principals and Practice Addison Wesley, Reading (1990)
Cross, R.G., Jain, A.K.: Markov Random Fields Texture Models. IEEE Trans. on Patt. An. and Mach. Intelligence PAMI-5(1), 25–39 (1983)
Panjwani, D.K., Healey, G.: Markov Random fields for Unsupervised Segmentation of Textured Colour Images. IEEE Trans. On Pattern Analysis and Machine Intelligence 17(10) (October 1995)
Chindaro, S., Sirlantzis, K., Deravi, F.: Colour Space Fusion for Texture Recognition. In: Proc. of the 4th EURASIP Conf. on Video/Image Processing and Multimedia Communications (EC-VIP-MC 2003), Zagreb, Croatia, July 2003, pp. 181–186 (2003)
Kittler, J.: Combining Classifiers: A Theoretical Framework. Pattern Analysis and Application 1, 18–27 (1998)
Yule, G.: On the association of attributes in statistics. Phil. Transaction, A 194, 257–319 (1900)
Sneath, P., Sokal, R.: Numerical Taxonomy. W.H. Freeman and Company, New York (1973)
Giacinto, G., Roli, F.: Design of effective neural network ensembles for image classification processes. Image and Vision Computing 19(9/10), 699–707 (2001)
Cunningham, P., Carney, J.: Diversity versus quality in classification ensembles based on feature selection. In: TCD-CS-200-02, Trinity College Dublin (2000)
Dietterich, T.: An experimental comparison of 3 methods for constructing ensembles of decision trees: Bagging, boosting and randomization. Machine Learning 40(2), 139–157 (2000)
Patridge, D., Krazanowski, W.J.: Software Diversity: Practical Statistics for its measurement and exploitation. Inf. and Software Tech. 39, 707–717 (1997)
VisTex, Colour Image Database: VisionTexture (2000), http://www.white.media.mit.edu/vismod/imagery/
Texture Library Database, http://textures.forrest.cz
Kleinbaum, D.G., Kupper, L.L.: Applied Regression Analysis and other Multivariable methods. Duxberry Press, North Scituate, ISBN 0-87872-139-8
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chindaro, S., Sirlantzis, K., Fairhurst, M. (2005). Analysis and Modelling of Diversity Contribution to Ensemble-Based Texture Recognition Performance. In: Oza, N.C., Polikar, R., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2005. Lecture Notes in Computer Science, vol 3541. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494683_39
Download citation
DOI: https://doi.org/10.1007/11494683_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26306-7
Online ISBN: 978-3-540-31578-0
eBook Packages: Computer ScienceComputer Science (R0)