Improving Pollen Classification with Less Training
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This document proposes methods to improve pollen classification with less training data. It discusses using transfer learning and active learning to apply classification rules learned from existing labeled samples to new unlabeled samples, reducing the need for experts to manually label large training sets. The methods are tested on a dataset of 9 pollen types, using 6 types as a source domain and 3 as a target, achieving 92% accuracy while reducing the labeling effort needed by up to 5 times.
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Improving Pollen Classification with Less Training
- 1. Improving Pollen Classification with Less Training Effort N. Rich Nguyen, Matina Donaldson-Matasci, and Min C. Shin WACV 2013: Clearwater Beach, FL
- 2. Why do we classify pollen? Image from youthhealthworld.com Help develop treatment for allergy, and also vital for crops
- 3. The world of Pollen A population of various pollen types under a microscope
- 4. Automatic Classification Automatic classification Manual labeling Goal: High accuracy with less labeling effort
- 5. Challenges Manual labeling Automatic classification Tedious and expensive Large training samples Labeling effort for skilled specialists
- 6. Transfer Learning source samples existing rule 1. Learn rules from existing (source) samples using AdaBoost
- 7. Transfer Learning source samples existing rule target sample transfer rule unlabeled sample 2. Apply rules to new (target) samples by modifying TaskTrAdaBoost
- 8. Active Learning source samples existing rule target sample transfer rule unlabeled sample selected sample 3. Develop a new selection criterion to focus on target samples
- 9. Active Learning source samples existing rule target sample transfer rule unlabeled sample selected sample updated rule 4. Ask expert for label, then update rules based on the new training set
- 10. Results 1. Select more target samples 2. Select better target samples Reducing training effort up to 5 times with 92% accuracy
- 11. THANK YOU! Poster #22: Improving Pollen Classification with Less Training Effort
- 12. BONUS SLIDES
- 13. III. Feature Computing Develop “spike count” to detect local minimum signal
- 14. Experiments Dataset Procedure 9 pollen types from bees 6 types as source (30+ samples) 5000 X 4000 image (1mm2) 3 types as target (3 samples) 10,000+ pollen grains 5 unlabeled samples X 50 iterations 770 grains labeled 30 replications Representative samples from different pollen types