Home   >   CSC-OpenAccess Library   >    Manuscript Information
Multiple Features Based Two-stage Hybrid Classifier Ensembles for Subcellular Phenotype Images Classification
Bailing Zhang, Tuan D. Pham
Pages - 176 - 193     |    Revised - 30-11-2010     |    Published - 20-12-2010
Volume - 4   Issue - 5    |    Publication Date - December 2010  Table of Contents
subcellular phenotype images classification, hybrid classifier, image feature extraction
Subcellular localization is a key functional characteristic of proteins. As an interesting ``bio-image informatics\'\' application, an automatic, reliable and efficient prediction system for protein subcellular localization can be used for establishing knowledge of the spatial distribution of proteins within living cells and permits to screen systems for drug discovery or for early diagnosis of a disease. In this paper, we propose a two-stage multiple classifier system to improve classification reliability by introducing rejection option. The system is built as a cascade of two classifier ensembles. The first ensemble consists of set of binary SVMs which generalizes to learn a general classification rule and the second ensemble, which also include three distinct classifiers, focus on the exceptions rejected by the rule. A new way to induce diversity for the classifier ensembles is proposed by designing classifiers that are based on descriptions of different feature patterns. In addition to the Subcellular Location Features (SLF) generally adopted in earlier researches, three well-known texture feature descriptions have been applied to cell phenotype images, which are the local binary patterns (LBP), Gabor filtering and Gray Level Coocurrence Matrix (GLCM). The different texture feature sets can provide sufficient diversity among base classifiers, which is known as a necessary condition for improvement in ensemble performance. Using the public benchmark 2D HeLa cell images, a high classification accuracy 96% is obtained with rejection rate $21\\%$ from the proposed system by taking advantages of the complementary strengths of feature construction and majority-voting based classifiers\' decision fusions.
CITED BY (5)  
1 Perner, P. (2015). Cognitive Aspects of Object Recognition–Recognition of Objects by Texture. Procedia Computer Science, 60, 391-402.
2 Zainudin, F. L., Mahamad, A. K., Saon, S., & Yahya, M. N. (2014, August). Comparison between GLCM and modified Zernike moments for material surfaces identification from photo images. In Computational Science and Technology (ICCST), 2014 International Conference on (pp. 1-4). IEEE.
3 Tahir, M., Khan, A., Majid, A., & Lumini, A. (2013). Subcellular localization using fluorescence imagery: utilizing ensemble classification with diverse feature extraction strategies and data balancing. Applied Soft Computing, 13(11), 4231-4243.
4 L. Small, J. Shelton, A. Alford, K. Bryant, G. Dozier and K. Washington, “Landmark-Based Local Binary Patterns for FaceRecognition”, Dozier Leading Biometrics Research, Association of Computer and Information Science/Engineering Departments at Minority Institutions (ADMI), 2011.
5 Zhang, B., & Gao, Y. (2011). Spectral regression dimension reduction for multiple features facial image retrieval. International Journal of Biometrics, 4(1), 77-101.
1 Google Scholar 
2 Academic Journals Database 
3 CiteSeerX 
4 refSeek 
5 iSEEK 
6 Socol@r  
7 ResearchGATE 
8 Libsearch 
9 Bielefeld Academic Search Engine (BASE) 
10 Scribd 
11 WorldCat 
12 SlideShare 
13 PdfSR 
A. Chebira, Y. Barbotin, C. Jackson, T. Merryman, G. Srinivasa, RF., Murphy and J. Kovacevic, “A multiresolution approach to automated classification of protein subcellular location images”. Bioinformatics, 8: 210, 2007.
B. Manjunath and W. Ma, “Texture Features for Browsing and Retrieval of Image Data”.IEEE Trans. on Pattern Analysis and Machine Intelligence, 18(8):, pp.837—842, 1996.
B. Zhang, “Classification of Subcellular Phenotype Images by Decision Templates for Classifier Ensemble”. International Conference on Computational Models for Life Sciences (CMLS-09), AIP Conf. Proc. 1210, pp.13-22, 2009.
C.-W. Hsu and C.-J. Lin, “A comparison on methods for multi-class support vector machines”. IEEE Transactions on Neural Networks, 13: pp.415—425, 2002.
C.K.Chow, “On optimum recognition error and reject tradeoff”. IEEE Trans. Inf. Theory, IT-16 (1), 41–46, 1970.
D.M.J Tax and R.P.W. Duin, “Growing a multi-class classifier with a reject option”, Pattern Recognition Letters, 29: pp. 1565-1570, 2008.
E.J. Roques and R.F. Murphy RF. “Objective Evaluation of Differences in Protein Subcellular Distribution”, Traffic, 3, Pages 61 – 65, 2002.
G. Fumera and F. Roli. Support Vector Machines with Embedded Reject Option, Int.Workshop on Pattern Recognition with Support Vector Machines (SVM2002), Springer,Niagara Falls, Canada, p.68-82, 2002.
G. Zhang, X. Huang, S.Z. Li, Y. Wang, and X. Wu, “Boosting Local Binary Pattern (LBP)-Based Face Recognition”. In Proc. Advances in Biometric Person Authentication: 5th Chinese Conference on Biometric Recognition, SINOBIOMETRICS 2004, Guangzhou,China. pp. 179-186, 2005.
H. Peng, “Bioimage informatics: a new area of engineering biology”. Bioinformatics, 24(17):pp. 1827—36, 2008.
J. Davis, M. Kakar, and C. Lim. “Controlling protein compartmentalization to overcome disease”. Pharm Res. 24(1): pp.17—27, 2007..
J. Shawe-Taylor and N. Cristianini, “Kernel methods for pattern analysis”. Cambridge University Press (2004).
K. Huang and R.F. Murphy,. “Boosting accuracy of automated classification of fluorescence microscope images for location proteomics”. BMC Bioinformatics, 5: 78, 2004.
L. Breiman, “Random Forests”. Machine Learning, 45, pp. 5–32, 2001.
L. Lam and C.Y. Suen, “Application of Majority Voting to Pattern Recognition: An Analysis of Its Behavior and Performance”, IEEE Transactions on Systems, Man, and Cybernetics -Part A: Systems and Human, 27: pp.553-568, 1997.
L. Wolf, T. Hassner and Y. Taigman, “Descriptor Based Methods in the Wild”. Faces in Real-Life Images workshop at the European Conference on Computer Vision (ECCV), Oct 2008.
L.I. Kuncheva, “Combining Pattern Classifiers: Methods and Algorithms”. Wiley-Interscience.,(2004).
L.Nanni, A. Lumini, Y. Lin, C. Hsu, and C. Lin, “Fusion of systems for automated cell phenotype image classification”. Expert Systems with Applications, 37: pp. 1556-1562, 2010.
M.V. Boland and R.F. Murphy, “A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells”.Bioinformatics, 17(12): pp.1213—23, 2001.
M.V. Boland, M. Markey and R.F. Murphy, “Automated Recognition of Patterns Characteristic of Subcellular Structures in Fluorescence Microscopy Images”. Cytometry, 33:pp. 366-375, 1998.
N. Holden and A.A. Freitas “A Hybrid PSO/ACO Algorithm for Discovering Classification Rules in Data Mining”, Journal of Artificial Evolution and Applications, 2008, Article ID 316145, 11 pages, 2008.
N. Orlov, J. Johnston, T. Macura, L. Shamir and I.Goldberg, “Computer Vision for Microscopy Applications. Vision Systems: Segmentation and Pattern Recognition”, Edited by:Goro Obinata and Ashish Dutta, pp.546, I-Tech, Vienna, Austria, June 2007
N.A. Hamilton, R.S. Pantelic, K. Hanson and R.D.Teasdale. “Fast automated cell phenotype image classification”. Bioinformatics, 8: pp. 110, 2007.
N.Giusti, F. Masulli, F., Sperduti, “A Theoretical and Experimental Analysis of a Two-Stage System for Classification”. IEEE Trans. on Pattern Analysis and Machine Intelligence, 24: pp.893–904,2002.
P. Pudil, J. Novovicova, S. Blaha, J. Kittler, “Multistage Pattern Recognition with Reject Option”. In: Proc. 11th IAPR Int. Conf. on Pattern Recognition, 2: pp.92-95, 1992.
R. Haralick “Statistical and Structural Approaches to Texture”,. Proceedings of the IEEE,67(5)} pp. 786-804, 1979.
R. Rifkin and A. Klautau, “In Defense of One-Vs-All Classification”. Journal of Machine Learning Research, 5: pp. 101-141, 2004.
R.M. Nosofsky, T.J. Palmeri and S.C. McKinley, “Rule-Plus-Exception Model of Classification Learning”. Psychological Review, 101, pp.53-79, 1994.
R.O. Duda, P.E. Hart and D.G. Stork,D.G. “Pattern classification”, Second Edition, John Wiley and Sons, New York, (2001).
R.P.W. Duin and D.M.J. Tax, “Classifier conditional posterior probabilities”. In: Amin, A., Dori,D., Pudil, P., Freeman, H. (eds.): Advances in Pattern Recognition. Lecture Notes in Computer Science 1451, Springer, Berlin, 611-619, 1998.
S. Maji, A.C. Berg, and J. Malik, . “Classification Using Intersection Kernel Support Vector Machines is efficient”. In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2008) ,Anchorage, Alaska, pp. 1-8, 2008.
T. Ahonen, A. Hadid and M. Pietikainen, “Face Description with Local Binary Patterns:Application to Face Recognition”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12): pp.2037-2041, 2006.
T. Ojala, M. Pietikainen and T. Maenpaa, “Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7): pp.971-987, 2002.
Z. Guo, L. Zhang and D. Zhang “A Completed Modeling of Local Binary Pattern Operator for Texture Classification”. accepted for IEEE Trans Image Process., preprint, 2010.
Dr. Bailing Zhang
Xi'an Jiaotong-Liverpool University - China
Dr. Tuan D. Pham
- Australia

View all special issues >>