Abstract
Multi-view learning is a very useful classification technique when multiple, conditionally independent feature sets are available in a dataset. In this paper multi-view learning is used to classify sequences of protein crystallization images that were obtained over a period of time, varying between a few hours to a few months. We introduce the use of the difference image features, along with the original image features, as a second feature set in classifying x-ray crystallography images, after arranging the images according to the timeline of an experiment. Usage of multi-view learning is proposed after carrying out experiments to determine the features that should be used in each view to increase classification accuracy. Random forests are used as the classifier in each view, as preliminary experiments have suggested that it provides higher classification accuracy in crystallography datasets. Accuracy of 97.2% was obtained using multi-view learning based on original and difference features, which is the highest obtained so far in the classification of protein crystallography images.
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
Publications.nigms.nih.gov. Chapter 2: X-ray Crystallography: Art Marries Science - The Structures of Life - Science Education - National Institute of General Medical Sciences (2011). http://publications.nigms.nih.gov/structlife/chapter2.html (cited 2012)
Hofmann, A., et al.: Methods of Molecular Analysis in the Life Sciences. Cambridge University Press (2014)
Gray, E.D., et al.: What is x-ray crystallography? n.d. http://www.chem.ed.ac.uk/bunsen_learner/bunsen_xray.html (cited 2012)
Newman, J., et al.: On the need for an international effort to capture, share and use crystallization screening data. Acta. Crystallogr. Sect. F Struct. Biol. Cryst. Commun. 68(Pt 3), 253–258 (2012)
Cumbaa, C., Jurisica, I.: Protein crystallization analysis on the World Community Grid. Journal of Structural and Functional Genomics 11(1), 61–69 (2010)
Lekamge, B.M.T., et al.: Classification of protein crystallisation images using texture-based statistical features. In: AIP Conference Proceedings, vol. 1559, no. 1, pp. 270–276 (2013)
Xu, C., Tao, D., Xu, C.: A Survey on Multi-view Learning (2013)
Mele, K., et al.: Using Time Courses To Enrich the Information Obtained from Images of Crystallization Trials. Crystal Growth & Design 14(1), 261–269 (2013)
Kotseruba, Y., Cumbaa, C.A., Jurisica, I.: High-throughput protein crystallization on the World Community Grid and the GPU. Journal of Physics: Conference Series 341(1), 012027 (2012)
Cumbaa, C., Jurisica, I.: Automatic classification and pattern discovery in high-throughput protein crystallization trials. J. Struct. Funct. Genomics 6(2–3), 195–202 (2005)
Buchala, S., Wilson, J.C.: Improved classification of crystallization images using data fusion and multiple classifiers. Acta Crystallographica Section D 64(8), 823–833 (2008)
Watts, D., Cowtan, K., Wilson, J.: Automated classification of crystallization experiments using wavelets and statistical texture characterization techniques. Journal of Applied Crystallography 41(1), 8–17 (2008)
Walker, C.G., Foadi, J., Wilson, J.: Classification of protein crystallization images using Fourier descriptors. Journal of Applied Crystallography 40(3), 418–426 (2007)
Vallotton, P., et al.: DroplIT, an improved image analysis method for droplet identification in high-throughput crystallization trials. Journal of Applied Crystallography 43(6), 1548–1552 (2010)
Mitchell, T.M.: Machine Learning, p. 45. McGraw Hill, Burr Ridge (1997)
Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn, p. 629. Elsevier Inc., Burlington (2011)
Aiping, W., et al.: An incremental extremely random forest classifier for online learning and tracking. In: 2009 16th IEEE International Conference on Image Processing (ICIP) (2009)
Shotton, J., Johnson, M., Cipolla, R.: Semantic texton forests for image categorization and segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, Anchorage, AK, p. 1–8 (2008)
Wang, Z., et al.: Feature Extraction via Multi-View Non-Negative Matrix Factorization with Local Graph Regularization
Hady, M.F.A., et al.: Multi-view forests based on Dempster-Shafer evidence theory: a new classifier ensemble method. In: Proceedings of the Fifth IASTED International Conference on Signal Processing, Pattern Recognition and Applications. ACTA Press, Innsbruck, pp. 18–23 (2008)
Li, S.-Y., Jiang, Y., Zhou, Z.-H.: Partial multi-view clustering. In: Twenty-Eighth AAAI Conference on Artificial Intelligence (2014)
Sun, S.: A survey of multi-view machine learning. Neural Computing and Applications 23(7–8), 2031–2038 (2013)
Wang, M., Hua, X.-S.: Active learning in multimedia annotation and retrieval: A survey. ACM Trans. Intell. Syst. Technol. 2(2), 1–21 (2011)
Settles, B.: Active Learning Literature Survey (2010)
Wang, W., Zhou, Z.-H.: Analyzing co-training style algorithms. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 454–465. Springer, Heidelberg (2007)
Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pp. 92–100. ACM, Madison (1998)
Hillebrand, M., Kreßel, U., Wöhler, C., Kummert, F.: Traffic Sign classifier adaption by semi-supervised co-training. In: Mana, N., Schwenker, F., Trentin, E. (eds.) ANNPR 2012. LNCS, vol. 7477, pp. 193–200. Springer, Heidelberg (2012)
Lazarova, G., Koychev, I.: A semi-supervised multi-view genetic algorithm. In: 2014 2nd International Conference on Artificial Intelligence, Modelling and Simulation (AIMS) (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Thamali Lekamge, B.M., Sowmya, A., Newman, J. (2016). Multi-view Learning for Classification of X-Ray Crystallography Images. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2016. Lecture Notes in Computer Science(), vol 9729. Springer, Cham. https://doi.org/10.1007/978-3-319-41920-6_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-41920-6_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-41919-0
Online ISBN: 978-3-319-41920-6
eBook Packages: Computer ScienceComputer Science (R0)