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Improving pollen classification with less training effort
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Improving pollen classification with less training effort

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Improving pollen classification with less training effort Improving pollen classification with less training effort Presentation Transcript

  • Improving Pollen Classification with Less Training Effort N. Rich Nguyen, Matina Donaldson-Matasci, and Min C. Shin WACV 2013: Clearwater Beach, FL
  • Why do we classify pollen? Image from youthhealthworld.comHelp develop treatment for allergy, and also vital for crops
  • The world of PollenA population of various pollen types under a microscope View slide
  • Automatic ClassificationAutomatic classification Manual labeling Goal: High accuracy with less labeling effort View slide
  • Challenges Manual labeling Automatic classificationTedious and expensive Large training samples Labeling effort for skilled specialists
  • Transfer Learning source samples existing rule1. Learn rules from existing (source) samples using AdaBoost
  • Transfer Learning source samples existing rule target sample transfer rule unlabeled sample2. Apply rules to new (target) samples by modifying TaskTrAdaBoost
  • Active Learning source samples existing rule target sample transfer rule unlabeled sample selected sample3. Develop a new selection criterion to focus on target samples
  • 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
  • Results 1. Select more target samples 2. Select better target samplesReducing training effort up to 5 times with 92% accuracy
  • 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 (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