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Dimitar Nikolov Nikolov Diana Dimitrova Tsankova

Abstract

The aim of the article is to investigate the features extraction from microscope images of pollens for a classification of honey on the base of its botanical origin. A filter-bank of Gabor filters (as a biologically inspired recognition system) is used to obtain features, which are then post-processed using normalization, down-sampling (by bicubic interpolation), and principal components analysis (PCA). PCA is used for reducing the features size and a proper visualization of the features extraction results. Microscope images from the European pollen database, including pollen images of linden, acacia, lavender, rapeseed, and thistle, are used to illustrate capabilities of the proposed features extraction approach. The performance of the proposed algorithm is evaluated by simulations in MATLAB environment.

Article Details

References

BDS 2673-89: Bee honey. Sofia, Bulgaria: State Committee for Standardization at the Council of Ministers, People's Republic of Bulgaria, 1989. [in Bulgarian]

Chudyk C., Castaneda H., Leger R., Yahiaoui I., Boochs F. Development of an Automatic Pollen Classification System Using Shape, Texture and Aperture Features. In: R. Bergmann, S. Gorg, G. Muller (Eds.): Proceedings of the LWA 2015 Workshops: KDML, FGWM, IR, and FGDB, 2015, 65-74. Trier, Germany.

Daood A., Ribeiro E., Bush M. Pollen Recognition Using Multi-Layer Feature Decomposition. Proceedings of the Twenty-Ninth International Florida Artificial Intelligence Research Society Conference, 2016, 26-31, Association for the Advancement of Artificial Intelligence.

Hu P., Zhao Y., Yang Z., Wang J. Recognition of Gray Character Using Gabor Filters. ISIF, 2002, 419-424. http://fusion.isif.org/proceedings/fusion02CD/pdffiles/papers/M3D03.pdf

Hubel D.H., Wiesel T.N. Receptive fields and functional architecture of monkey striate cortex. The Journal of physiology, 1968, 195(1): 215-243. https://doi.org/10.1113/jphysiol.1968.sp008455

Jarrett K., Kavukcuoglu K., Ranzato M., LeCun Y. What is the best multi-stage architecture for object recognition? IEEE 12th International Conference on Computer Vision, 2009, 2146-2153. https://doi.org/10.1109/ICCV.2009.5459469

Jolliffe I.T. Principal Component Analysis. (Second ed.). In: Springer Series in Statistics. New York, Springer-Verlag, 2002. https://doi.org/10.1007/978-1-4757-1904-8

Jones J.P., Palmer L.A. An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. Journal Neurophysiology, 1987, 58(6): 1233-1258. https://doi.org/10.1152/jn.1987.58.6.1233

Kaya Y., Erez M.E., Karabacak O., Kayci L., Fidan M. An automatic identification method for the comparison of plant and honey pollen based on GLCM texture features and artificial neural network. Grana, 2013, 52(1): 71-77. https://doi.org/10.1080/00173134.2012.754050

Kim H., Drake B.L., Park H. Multiclass classifiers based on dimension reduction with generalized LDA. Pattern Recognition, 2007, 40(11): 2939–2945. https://doi.org/10.1016/j.patcog.2007.03.002

Li P., Treloar W.J., Flenley J.R., Empson L. Towards Automation of Palynology 2: The Use of Texture Measures and Neural Network Analysis for Automated Identification of Optical Images of Pollen Grains. Journal of Quaternary Science, 2004, 19(8): 755–762. https://doi.org/10.1002/jqs.874

Li Y., Yang H. Honey Discrimination Using Visible and Near-Infrared Spectroscopy. International Scholarly Research Network, ISRN Spectroscopy, 2012, 2012: 4 pages, Article ID 487040. https://dx.doi.org/10.5402/2012/487040

Marcos J.V., Nava R., Cristóbal G., Redondo R., Escalante-Ramírez B., Bueno G., Déniz Ó., González-Porto A., Pardo C., Chung F., Rodríguez T. Automated pollen identification using microscopic imaging and texture analysis. Micron, 2015, 68: 36-46. https://doi.org/10.1016/j.micron.2014.09.002

MicrolabNW Photomicrograph Gallery. (Bee Pollen, Guide to pollens in general). Available from: http://www.microlabgallery.com/

Movellan J. R. Tutorial on Gabor filters. Open Source Document, 2002.

Ordinance 9 of 22.06.2005 on the conditions and procedure for approval and registration of wax processing plants and production of wax foundations as well as of the enterprises for production and trade with honey and bee products, issued by the Minister of Agriculture and Forests, prom., SG, no. 54 of 1.07.2005. [in Bulgarian]

PalDat - Palynological Database, an online publication on recent pollen. (Search). 2000. Available from: https://www.paldat.org/search/A

Persano Oddo L., Bogdanov S. Determination of Honey Botanical Origin: Problem and Issues. Apidologie, 2004, 35 (special issue) (Suppl. 1): 2-3. https://doi.org/10.1051/apido:2004044

Pollen-Wiki: Digital Pollen Atlas. (List of Species), 2013. [in German]. Available from: http://pollen.tstebler.ch/MediaWiki/index.php?title=Artenliste

Pollenatlas. Medical University of Vienna. [in German]. Available from: https://www.pollenwarndienst.at/aerobiologie/pollenatlas.html

Robbins R.J. Phenolis acids in foods: An overview of analytical methodology. Journal of Agricultural and Food Chemistry, 2003,51(10):2866–2887. https://doi.org/10.1021/jf026182t

Rodriguez-Damian M., Cernadas E., Formella A., Fernandez-Delgado M., Sa-Otero P.D. Automatic detection and classification of grains of pollen based on shape and texture. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 2006, 36(4): 531–542. https://doi.org/10.1109/TSMCC.2005.855426

Ruoff K. Authentication of the Botanical Origins of Honey. A Dissertation for the degree of Doctor of Sciences. University of Helsinki, 2006, p.32.

Ruoff K., Karoui R., Dufour E., Luginbuhl W., Bosset J.O., Bogdanov S., Amado R. Authentication of the botanical origin of honey by front-face fluorescence spectroscopy. A preliminary study. J Agric Food Chem., 2005, 53(5): 1343-1347. https://doi.org/10.1021/jf048384q

Science & Plants for Schools - Pollen Image Library. (Pollen images in alphabetical order). Copyright Science & Plants for Schools, www.saps.org.uk, 2011. Available from: http://www-saps.plantsci.cam.ac.uk/pollen/index.htm

Sergiel I., Pohl P., Biesaga M., Mironczyk A. Suitability of three-dimensional synchronous fluorescence spectroscopy for fingerprint analysis of honey samples with reference to their phenolic profiles. Food Chemistry, 2014, 145: 319–326. https://doi.org/10.1016/j.foodchem.2013.08.069

Serre T., Riesenhuber M. Realistic modeling of simple and complex cell tuning in the hmax model, and implications for invariant object recognition in cortex. Technical report, DTIC Document, Massachusetts institute of technology — computer science and artificial intelligence laboratory. 2004. AI Memo 2004-017, CBCL Memo 239.

Statistics Toolbox™ User’s Guide, R2014a, © Copyright 1993–2014 by the MathWorks, Inc.

Treloar W.J., Taylor G.E., Flenley J.R. 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, 2004, 19(8): 745–754. https://doi.org/10.1002/jqs.871

Zhang Y., Fountain D.W., Hodgson R.M., Flenley J.R., Gunetileke S. Towards Automation of Palynology 3: Pollen Pattern Recognition using Gabor Transforms and Digital Moments. Journal of Quaternary Science, 2004, 19(8): 763–768. https://doi.org/10.1002/jqs.875

How to Cite
NIKOLOV, Dimitar Nikolov; TSANKOVA, Diana Dimitrova. Features Extraction for Pollen Recognition Using Gabor Filters. Food Science and Applied Biotechnology, [S.l.], v. 1, n. 2, p. 86-95, oct. 2018. ISSN 2603-3380. Available at: <https://www.ijfsab.com/index.php/fsab/article/view/11>. Date accessed: 19 dec. 2018. doi: https://doi.org/10.30721/fsab2018.v1.i2.11.