Logistic Model Tree and Expectation-Maximization for Pollen Recognition and Grouping

Authors: Endrick Barnacin, Jean-Luc Henry, Jack Molinié, Jimmy Nagau, Hélène Delatte, Gérard Lebreton

Abstract:

Palynology is a field of interest for many disciplines. It has multiple applications such as chronological dating, climatology, allergy treatment, and even honey characterization. Unfortunately, the analysis of a pollen slide is a complicated and time-consuming task that requires the intervention of experts in the field, which is becoming increasingly rare due to economic and social conditions. So, the automation of this task is a necessity. Pollen slides analysis is mainly a visual process as it is carried out with the naked eye. That is the reason why a primary method to automate palynology is the use of digital image processing. This method presents the lowest cost and has relatively good accuracy in pollen retrieval. In this work, we propose a system combining recognition and grouping of pollen. It consists of using a Logistic Model Tree to classify pollen already known by the proposed system while detecting any unknown species. Then, the unknown pollen species are divided using a cluster-based approach. Success rates for the recognition of known species have been achieved, and automated clustering seems to be a promising approach.

Keywords: Pollen recognition, logistic model tree, expectation-maximization, local binary pattern.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.3669283

References:

[1] Zhang, Y., D. W. Fountain, R. M. Hodgson, J. R. Flenley, and S. Gunetileke. “Towards Automation of Palynology 3: Pollen Pattern Recognition Using Gabor Transforms and Digital Moments.” Journal of Quaternary Science 19, no. 8 (2004): 763–768. doi:10.1002/jqs.875.
[2] Treloar, W. J., G. E. Taylor, and J. R. Flenley. “Towards Automation of Palynology 1: Analysis of Pollen Shape and Ornamentation Using Simple Geometric Measures, Derived from Scanning Electron Microscope Images.” Journal of Quaternary Science 19, no. 8 (2004): 745–754. doi:10.1002/jqs.871.
[3] Ticay-Rivas, Jaime R., Marcos del Pozo-Baños, Carlos M. Travieso, Jorge Arroyo-Hernández, Santiago T. Pérez, Jesús B. Alonso, and Federico Mora-Mora. “Pollen Classification Based on Geometrical, Descriptors and Colour Features Using Decorrelation Stretching Method.” Artificial Intelligence Applications and Innovations (2011): 342–349. doi:10.1007/978-3-642-23960-1_41.
[4] C. Chudyk, H. Castaneda, R. Léger, I. Yahiaoui and F. Boochs, "Development of an Automatic Pollen Classification System Using Shape, Texture and Aperture Features," LWA 2015 Workshops: KDML, FGWM, IR, and FGDB, 2015 .
[5] G. Lozano-Vega, "Image-based detection and classification of allergenic pollen,"2015.
[6] Chen, Chun, Emile A. Hendriks, Robert P. W. Duin, Johan H. C. Reiber, Pieter S. Hiemstra, Letty A. de Weger, and Berend C. Stoel. “Feasibility Study on Automated Recognition of Allergenic Pollen: Grass, Birch and Mugwort.” Aerobiologia 22, no. 4 (December 2006): 275–284. doi:10.1007/s10453-006-9040-0.
[7] Nguyen, Nhat Rich, Matina Donalson-Matasci, and Min C. Shin. “Improving Pollen Classification with Less Training Effort.” 2013 IEEE Workshop on Applications of Computer Vision (WACV) (January 2013). doi:10.1109/wacv.2013.6475049.
[8] Kaya, Yılmaz, S. Mesut Pınar, M. Emre Erez, Mehmet Fidan, and James B. Riding. “Identification of Onopordumpollen Using the Extreme Learning Machine, a Type of Artificial Neural Network.” Palynology 38, no. 1 (January 2, 2014): 129–137. doi:10.1080/09500340.2013. 868173.
[9] France, I, A.W.G Duller, G.A.T Duller, and H.F Lamb. “A New Approach to Automated Pollen Analysis.” Quaternary Science Reviews 19, no. 6 (February 2000): 537–546. doi:10.1016/s0277-3791(99)00021-9.
[10] Ronneberger, Olaf, Qing Wang, and Hans Burkhardt. “3D Invariants with High Robustness to Local Deformations for Automated Pollen Recognition.” Pattern Recognition (n.d.): 425–435. doi:10.1007/978-3-540-74936-3_43.
[11] Daood, Amar, Eraldo Ribeiro, and Mark Bush. “Pollen Recognition Using a Multi-Layer Hierarchical Classifier.” 2016 23rd International Conference on Pattern Recognition (ICPR) (December 2016). doi:10.1109/icpr.2016.7900109.
[12] Daood, Amar, Eraldo Ribeiro, and Mark Bush. “Pollen Grain Recognition Using Deep Learning.” Lecture Notes in Computer Science (2016): 321–330. doi:10.1007/978-3-319-50835-1_30.
[13] Daood, Amar, Ribeiro, Eraldo, AND Bush, Mark. "Sequential Recognition of Pollen Grain Z-Stacks by Combining CNN and RNN" Florida Artificial Intelligence Research Society Conference (2018): n. pag. Web. 30 Jul. 2019
[14] Landwehr, Niels, Mark Hall, and Eibe Frank. “Logistic Model Trees.” Lecture Notes in Computer Science (2003): 241–252. doi:10.1007/978-3-540-39857-8_23.
[15] Dempster, A. P., N. M. Laird, and D. B. Rubin. “Maximum Likelihood from Incomplete Data Via the EM Algorithm.” Journal of the Royal Statistical Society: Series B (Methodological) 39, no. 1 (September 1977): 1–22. doi:10.1111/j.2517-6161.1977.tb01600.x.
[16] G. Erdtman, "The acetolysis method, a revised description." Svensk Botanisk Tidskrift, vol. 54, (1960).
[17] Otsu, Nobuyuki. “A Threshold Selection Method from Gray-Level Histograms.” IEEE Transactions on Systems, Man, and Cybernetics 9, no. 1 (January 1979): 62–66. doi:10.1109/tsmc.1979.4310076.
[18] Gonzalez, Rafael C., Richard E. Woods, and Barry R. Masters. “Digital Image Processing, Third Edition.” Journal of Biomedical Optics 14, no. 2 (2009): 029901. doi:10.1117/1.3115362, pp. 407–413.
[19] Ojala, T., M. Pietikainen, and D. Harwood. “Performance Evaluation of Texture Measures with Classification Based on Kullback Discrimination of Distributions.” Proceedings of 12th International Conference on Pattern Recognition (n.d.). doi:10.1109/icpr.1994.576366.
[20] Joachims, Thorsten. “Text Categorization with Support Vector Machines: Learning with Many Relevant Features.” Lecture Notes in Computer Science (1998): 137–142. doi:10.1007/bfb0026683.