Chapter 12 Pollen Image Classification Using the Classifynder System: Algorithm Comparison and a Case Study on New Zealand Honey Ryan Lagerstrom, Katherine Holt, Yulia Arzhaeva, Leanne Bischof, Simon Haberle, Felicitas Hopf, and David Lovell Abstract 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 R. Lagerstrom () • Y. Arzhaeva • L. Bischof Digital Productivity Flagship, CSIRO, Locked Bag 17, North Ryde, Sydney, NSW 1670, Australia e-mail: ryan.lagerstrom@csiro.au; yulia.arzhaeva@csiro.au; leanne.bischof@csiro.au K. Holt Institute of Natural Resources, Massey University, PB 11222, Palmerston North 4442, New Zealand e-mail: k.holt@massey.ac.nz S. Haberle • F. Hopf School of Culture, History and Language, H C Coombs Blg 9, The Australian National University, Canberra, ACT 0200, Australia e-mail: simon.haberle@anu.edu.au; felicitas.hopf@anu.edu.au D. Lovell Digital Productivity Flagship, CSIRO, GPO Box 664, Canberra, ACT 2601, Australia e-mail: david.lovell@csiro.au © Springer International Publishing Switzerland 2015 C. Sun et al. (eds.), Signal and Image Analysis for Biomedical and Life Sciences, Advances in Experimental Medicine and Biology 823, DOI 10.1007/978-3-319-10984-8_12 207