A comparison of classification algorithms within the Classifynder pollen imaging system

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...

[1]  A.W.G. Duller,et al.  A new approach to automated pollen analysis , 2000 .

[2]  R. M. Hodgson,et al.  Progress towards an automated trainable pollen location and classifier system for use in the palynology laboratory , 2011 .

[3]  Monique Thonnat,et al.  Development of a semi-automatic system for pollen recognition , 2002 .

[4]  E. Cernadas,et al.  Computer-aided identification of allergenic species of Urticaceae pollen , 2004 .

[5]  J. Shulmeister,et al.  New Zealand chironomids as proxies for human-induced and natural environmental change: Transfer functions for temperature and lake production (chlorophyll a) , 2006 .

[6]  Suann Yang,et al.  Counting pollen grains using readily available, free image processing and analysis software. , 2009, Annals of botany.

[7]  O. Ronneberger,et al.  Automated pollen recognition using 3D volume images from fluorescence microscopy , 2002 .

[8]  E. C. Stillman,et al.  The needs and prospects for automation in palynology , 1996 .

[9]  J. R. Flenley,et al.  Towards automation of palynology 3: pollen pattern recognition using Gabor transforms and digital moments , 2004 .

[10]  Donald P. Greenberg,et al.  Color spaces for computer graphics , 1978, SIGGRAPH.

[11]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[12]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[13]  Lionel Carter,et al.  Towards a climate event stratigraphy for New Zealand over the past 30 000 years (NZ‐INTIMATE project) , 2007 .

[14]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .