Improving Pollen Classification   with Less Training Effort N. Rich Nguyen, Matina Donaldson-Matasci,              and Min...
Why do we classify pollen?                                           Image from youthhealthworld.comHelp develop treatment...
The world of PollenA population of various pollen types under a microscope
Automatic ClassificationAutomatic classification         Manual labeling    Goal: High accuracy with less labeling effort
Challenges   Manual labeling           Automatic classificationTedious and expensive         Large training samples       ...
Transfer Learning                                               source samples                                            ...
Transfer Learning                                                 source samples                                          ...
Active Learning                                                 source samples                                            ...
Active Learning                                                      source samples                                       ...
Results  1. Select more target samples   2. Select better target samplesReducing training effort up to 5 times with 92% ac...
THANK YOU!Poster #22: Improving Pollen Classification with Less Training Effort
BONUS SLIDES
III. Feature ComputingDevelop “spike count” to detect local minimum signal
ExperimentsDataset                    Procedure9 pollen types from bees   6 types as source (30+ samples)5000 X 4000 image...
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Improving pollen classification with less training effort

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Improving pollen classification with less training effort

  1. 1. Improving Pollen Classification with Less Training Effort N. Rich Nguyen, Matina Donaldson-Matasci, and Min C. Shin WACV 2013: Clearwater Beach, FL
  2. 2. Why do we classify pollen? Image from youthhealthworld.comHelp develop treatment for allergy, and also vital for crops
  3. 3. The world of PollenA population of various pollen types under a microscope
  4. 4. Automatic ClassificationAutomatic classification Manual labeling Goal: High accuracy with less labeling effort
  5. 5. Challenges Manual labeling Automatic classificationTedious and expensive Large training samples Labeling effort for skilled specialists
  6. 6. Transfer Learning source samples existing rule1. Learn rules from existing (source) samples using AdaBoost
  7. 7. Transfer Learning source samples existing rule target sample transfer rule unlabeled sample2. Apply rules to new (target) samples by modifying TaskTrAdaBoost
  8. 8. Active Learning source samples existing rule target sample transfer rule unlabeled sample selected sample3. Develop a new selection criterion to focus on target samples
  9. 9. Active Learning source samples existing rule target sample transfer rule unlabeled sample selected sample updated rule4. Ask expert for label, then update rules based on the new training set
  10. 10. Results 1. Select more target samples 2. Select better target samplesReducing training effort up to 5 times with 92% accuracy
  11. 11. THANK YOU!Poster #22: Improving Pollen Classification with Less Training Effort
  12. 12. BONUS SLIDES
  13. 13. III. Feature ComputingDevelop “spike count” to detect local minimum signal
  14. 14. ExperimentsDataset Procedure9 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 iterations770 grains labeled 30 replications Representative samples from different pollen types

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