Bigelow: Plankton Classification

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Bigelow Plankton Classification
CMPSCI 570/670 Spring 2006 Marwan (Moe)
Mattar www.cs.umass.edu/mmattar mmattar_at_cs.umass.
edu
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meet the folks

• Collaboration between,
• Computer Vision Lab, UMass, Amherst, MA
• Machine Learning Lab, UMass, Amherst, MA
• Bigelow Labs for Ocean Sciences, Boothbay Harbor,
  ME
• Coastal Fisheries Institute, LSU, Baton Rouge, LA

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overview

• Automatic classification of plankton (phyto- and
  zoo-) collected in-situ
• Why is this important?
• Understanding of global ecology
• Early detection of harmful algal blooms
• Bio-terrorism countermeasures

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sea-critters
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phyto-plankton

• What are phyto-plankton?
• They are microscopic plants that live in the sea,
  sometimes called grasses of the sea
• Since phytoplankton depend upon certain
  conditions for growth, they are a good indicator
  of change in their environment
• Consume carbon dioxide and produce oxygen, hence
  effect average temperature
• First link of the food chain for all marine
  creatures, so their survival is of great
  importance
• Can be imaged using Flow Cytometer And Microscope
  (FlowCAM)
• Data collection

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collecting images

• At least a 3-4 day process
• One day preparing for your trip, packing and
  travelling to your point of departure
• All of the next day is spent out in sea
  collecting data and then driving your samples
  back to the lab
• At least another day or two is spent
  hand-labelling a very, very small number of the
  phyto-plankton images
• We would like to relieve marine biologists from
  the third step.
• An active marine biologist has more data than
  they can hand-label in their lifetime.

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1. go out to sea
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2. collect samples
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3. flowcam in action
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4. zoom in
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5. analyze output
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data set

• 982 training images belonging to 13 classes
• Initial set had many more images from a lot more
  classes

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big picture
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segmentation

• Step 1 Perform segmentation

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feature extraction

• Step 2 Compute features
• Simple Shape (9) area, perimeter, compactness,
  convexity, eigenratio, rectangularity, of CC,
  mean area of CC and std of area of CC
• Moments-based (12) mean, variance, skewness,
  kurtosis and entropy of intensity distribution
  and 7 moment invariants
• Texture features??
• N.B. Almost all the features are invariant to
  scale and rotation. Which ones are not?

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classifier

• Step 3 Train Support Vector Machine classifier
• 10 fold cross validation
• Stratified cross validation??
• Polynomial kernel performed the best
• 2nd degree polynomial performed better than a
  linear classifier
• 3rd degree polynomial over-fit
• Overall best result 66 using 21 features

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issues in real-world problems

• Errors in labelling
• Noisy images at low resolution
• FlowCAM is very efficient and has a wide field of
  view
• Test-time speed
• Not a 0-1 loss
• Test data are not sampled IID
• Null-class classification

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zoo-plankton

• Larger marine animals
• Feed on phyto-plankton
• Can be imaged using Video Plankton Recorder (VPR)
• Data set contains 1826 images from 14 classes
• Full set contained a lot more images from more
  classes
• Images!!

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object recognition

• Other variants of the problem include
• Object of interest is in a cluttered background
• More than one object present in an image, either
  detect presence or quantity
• Look at standard data sets that the vision
  community uses to evaluate algorithms
• MIT Object Database
• Caltech-101
• ETH-80
• Coil-100 (old but still useful for some aspects)

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Thank You!
Questions?