Abstract
In this chapter we make an extensive study of different state-of-the-art classifiers for building an heterogeneous ensemble for sub-cellular image classification. As features for representing each image we used local ternary patterns. Our aim is to show that it is possible to boost the performance of a stand-alone texture descriptor (here we use the high performance method named local ternary patterns) by an heterogeneous ensemble. First, we compare different classification approaches (different kind of boosting; SVM with various kernels; diverse recent ensemble of decision trees...) in five datasets; then, we show that an heterogeneous ensemble, based on the fusion of different classifiers, performs consistently well across all the tested datasets. The most important result is showing that some very recent approaches and our proposed ensemble outperform also SVM classifier (the well known and widely used LibSVM implementation), even when both kernel selection and the various SVM parameters are carefully tuned. Finally we validated our ensemble also using several datasets from the UCI Repository and other standard pattern classification problems. The Matlab code of the classifiers used in the proposed ensemble is available at bias.csr.unibo.it/nanni/HET.rar.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The Matlab implementation is available in the tool of this paper.
- 2.
Download from http://ome.grc.nia.nih.gov/iicbu2008/hela/index.html
- 3.
Download from http://locate.imb.uq.edu.au/
- 4.
Download from http://ome.grc.nia.nih.gov/iicbu2008/hela/index.html#cho
- 5.
Datasets and descriptions available at http://archive.ics.uci.edu/ml/ (link accessed 21 July 2011).
References
Kuncheva, L.I.,Whitaker, C.J.: Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach. Learn. 51(2), 181–207 (2003)
Kittler, J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 226–239 (1998)
Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)
Bologna, G., Appel, R.d.: A comparison study on protein fold recognition. In: The 9th International Conference on Neural Information Processing, pp. 2492–2496. Singapore (2002)
Martínez-Muñoz, G., Suárez, A.: Switching class labels to generate classification ensembles. Pattern Recog. 38(10), 1483–1494 (2005)
Melville, P., Mooney, R.J.: Creating diversity in ensembles using artificial data. Inf. Fusion 6, 99–111 (2004)
Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832–844 (1998)
Tumer, K., Oza, N.C.: Input decimated ensembles. Pattern Anal. Appl. 6(1), 65–77 (2003)
Nanni, L., Lumini, A.: Ensemble generation and feature selection for the identification of students with learning disabilities. Expert Syst. Appl. 36(2), 3896–900 (2009)
Breiman, L.: Random forest. Mach. Learn. 45(1), 5–32 (2001)
Kotsiansis, S., Tampakas, V.: Combining heterogeneous classifiers: a recent overview. J. Converg. Inf. Technol. 6(10), 164–172 (2011)
Rodriguez, J.J., Kuncheva, L.I., Alonso, C.J.: Rotation forest: a new classifier ensemble method. IEEE Trans. Pattern Anal. Mach. Intell. 28(10), 1619–1630 (2006)
Freund, Y.: RE S. A decision-theoretic generalization of on-line learning and an applications to boosting. J. Comput. Syst. Sci. 55(1), 119–39 (1997)
Rasmussen, C.E., Williams, C.: Gaussian processes for machine learning, The MIT Press, Boston (2006)
Cevikalp, H., Triggs, B., Yavuz, H.S., Küçük, Y., Küçük, M., Barkana, A.: Large margin classifiers based on affine hulls. Neurocomput. 73(16–18), 3160–3168 (2010)
Nanni, L., Brahnam, S., Lumini, A.: Matrix representation in pattern classification. Expert Syst. Appl. 39(3), 3031–3036 (2012)
Aydogan, D.B., Hannula, M., Arola, T., Dastidar, P., Hyttinen, J.: 2D texture based classification, segmentation and 3D orientation estimation of tissues using DT-CWT feature extraction methods. Data Knowl. Eng. 68(12), 1383–1397 (2009)
Tuceryan, M.: Texture analysis. In: Chen C.H., Pau L.F., Wang P.S.P. (eds.) The Handbook of Pattern Recognition and Computer Vision, pp. 207–248. World Scientific Publishing Co, Singapore (1998)
Tamura, H., Mori, S.: Textural features corresponding to visual perception. IEEE Trans. Syst. Man Cybern. 75(6), 460–73 (1978)
Haralick, R.: Statistical and structural approaches to texture. P IEEE 67(5), 786–804 (1979)
Sklansky, J.: Image segmentation and feature extraction. IEEE Trans. Syst. Man Cybern. 75(4), 237–247 (1978)
Hall-Beyer, M.: The GLCM Tutorial [Internet]. Version 2.10. 2007; Available from: www.fp.ucalgary.ca/mhallbey/tutorial.htm
Ahonen, T., Hadid, A., Pietikäinen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)
Nanni, L., Lumini, A.: Local binary patterns for a hybrid fingerprint matcher. Pattern Recogn. 41(11), 3461–3466 (2008)
Vécsei, A., Amann, G., Hegenbart, S., Liedlgruber, M., Uhl, A.: Automated Marsh-like classification of celiac disease in children using local texture operators. Comput. Biol. Med. 41(6), 313–325 (2011)
Oliver, A., Lladó, X., Freixenet, J., Martí, J.: False positive reduction in mammographic mass detection using local binary patterns. MICCAI Int. Conf. Med. Image Comput. Compu.Assist. Intervention 10(Pt 1), 286–93 (2007)
Nanni, L., Lumini, A.: A reliable method for cell phenotype image classification. Artif. Intell. Med. 43(2), 87–97 (2008)
Paci, M., Nanni, L., Lahti, A., Aalto-Setälä, K., Hyttinen, J., Severi S.: Non-binary coding for texture descriptors in sub-cellular and stem cell image classification, Curr. Bioinform., Oak Park (2012)
Haralick R, Dinstein, Shanmugam K.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC-3, 610–621 (1973)
Toennies, KD.: Guide to medical image analysis: methods and algorithms. Springer, New York (2012)
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. 24(7), 971–987 (2002)
Fu, X., Wei, W.: Centralized binary patterns embedded with image euclidean distance for facial expression recognition. In: Proceedings of the 2008 Fourth International Conference on Natural Computation, Vol. 04, pp. 115–119. IEEE Computer Society, Washington (2008)
Zhang, B., Gao, Y., Zhao, S., Liu, J.: Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor. Trans. Img. Proc. 19(2), 533–544 (2010)
Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Process. 19(6), 1657–1663 (2010)
Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. 19(6), 1635–1650 (2010)
Boland, M.V., Murphy, R.F.: A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells. Bioinform. 17(12), 1213–1223 (2001)
Fink, J.L., Aturaliya, R.N., Davis, M.J., Zhang. F., Hanson, K., Teasdale, M.S., et al.: Locate: a mouse protein subcellular localization database. Nucleic Acids Res. 34(D) 213–217 (2006)
Boland, M.V., Markey, M.K., Murphy, R.F.: Automated recognition of patterns characteristic of subcellular structures in fluorescence microscopy images. Cytometry 33(3), 366–375 (1998)
Bache, K., Lichman, M.: UCI machine learning repository, University of California, Irvin (2010)
Jantzen, J., Norup, J., Dounias, G., Bjerregaard, B.: “Pap-smear benchmark data for pattern classification”, in nature inspired Smart information systems. Albufeira, Portugal (2005)
Ojansivu, V., Heikkilä, J.: “Blur insensitive texture classification using local phase quantization”, in Lect Notes Comput SC, pp. 236–243 Springer, Berlin (2008)
Fang, Y., Guo, Y., Feng, Y., Li, M.: Predicting DNA-binding proteins: approached from Chou’s pseudo amino acid composition and other specific sequence features. Amino Acids 34(1), 103–109 (2008)
Rohde, D.J., Drinkwater, M.J., Gallagher, M.R., Pimbblet, K.A.: Matching of catalogues by probabilistic pattern classification. Mon. Not. R. Astron. Soc. 369(1), 2–14 (2006)
Zhang, C.-X., Zhang, J.-S.: RotBoost: a technique for combining rotation Forest and adaBoost. Pattern Recogn. Lett. 29(10), 1524–1536 (2008)
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
Segata, N., Blanzieri, E.: Fast and scalable local kernel machines. J. Mach. Learn. Res. 11, 1883–1926 (2010)
Kittler, J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 226–239 (1998)
Wolpert, D.H., Macready, W.G.: Coevolutionary free lunches. IEEE Trans. Evol. Comput. 9(6), 721–735 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Nanni, L., Paci, M., Severi, S. (2014). Heterogeneous Ensemble of Classifiers for Sub-Cellular Image Classification Based on Local Ternary Patterns. In: Brahnam, S., Jain, L., Nanni, L., Lumini, A. (eds) Local Binary Patterns: New Variants and Applications. Studies in Computational Intelligence, vol 506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39289-4_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-39289-4_6
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39288-7
Online ISBN: 978-3-642-39289-4
eBook Packages: EngineeringEngineering (R0)