Pollen image classification using the classifynder system: Algorithm comparison and a case study on New Zealand honey

Lagerstrom, R., Holt, K., Arzhaeva, Y., Bischof, L., Haberle, S., Hopf, F., & Lovell, D. R. (2015) Pollen image classification using the classifynder system: Algorithm comparison and a case study on New Zealand honey. In Advances in Experimental Medicine and Biology. Springer New York LLC, pp. 207-226.

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We describe an investigation into how Massey University’s Pollen Classifynder can accelerate the understanding of pollen and its role in nature. The Classifynder is an imaging microscopy system that can locate, image and classify slide based pollen samples. Given the laboriousness of purely manual image acquisition and identification it is vital to exploit assistive technologies like the Classifynder to enable acquisition and analysis of pollen samples. It is also vital that we understand the strengths and limitations of automated systems so that they can be used (and improved) to compliment the strengths and weaknesses of human analysts to the greatest extent possible. This article reviews some of our experiences with the Classifynder system and our exploration of alternative classifier models to enhance both accuracy and interpretability. Our experiments in the pollen analysis problem domain have been based on samples from the Australian National University’s pollen reference collection (2,890 grains, 15 species) and images bundled with the Classifynder system (400 grains, 4 species). These samples have been represented using the Classifynder image feature set.We additionally work through a real world case study where we assess the ability of the system to determine the pollen make-up of samples of New Zealand honey. In addition to the Classifynder’s native neural network classifier, we have evaluated linear discriminant, support vector machine, decision tree and random forest classifiers on these data with encouraging results. Our hope is that our findings will help enhance the performance of future releases of the Classifynder and other systems for accelerating the acquisition and analysis of pollen samples.

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ID Code: 79855
Item Type: Book Chapter
Keywords: Automation, Image analysis, Machine learning, Palynology, Pollen, Statistical classification, accuracy, Article, artificial neural network, case study, classification algorithm, classifynder system, computer model, computer system, discriminant analysis, intermethod comparison, microscopy, New Zealand, pollen analysis, statistical model, support vector machine, taxonomic identification
DOI: 10.1007/978-3-319-10984-8_12
ISBN: 978-3-319-10984-8
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Springer
Deposited On: 07 Jan 2015 04:33
Last Modified: 15 Mar 2016 04:54

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