A comparison of classification algorithms within the Classifynder pollen imaging system

Lagerstrom, R., Arzhaeva, Y., Bischof, L., Haberle, S., Hopf, F., & Lovell, D. R. (2013) A comparison of classification algorithms within the Classifynder pollen imaging system. In Proceedings of the 2013 AIP Conference, AIP - American Institute of Physics, pp. 250-259.

View at publisher


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 (2890 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. 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. © 2013 AIP Publishing LLC.

Impact and interest:

3 citations in Scopus
2 citations in Web of Science®
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

42 since deposited on 07 Jan 2015
12 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 79860
Item Type: Conference Paper
Refereed: No
Keywords: automation, classification, palynology, Pollen
DOI: 10.1063/1.4825017
ISBN: 0094243X (ISSN)
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: AIP
Deposited On: 07 Jan 2015 04:11
Last Modified: 25 Jun 2017 04:27

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page