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Bigelow: Plankton Classification

Bigelow: Plankton Classification. CMPSCI: 570/670 Spring 2006 Marwan (Moe) Mattar www.cs.umass.edu/~mmattar mmattar@cs.umass.edu. meet the folks. Collaboration between, Computer Vision Lab, UMass, Amherst, MA Machine Learning Lab, UMass, Amherst, MA

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Bigelow: Plankton Classification

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  1. Bigelow: Plankton Classification CMPSCI: 570/670 Spring 2006 Marwan (Moe) Mattar www.cs.umass.edu/~mmattar mmattar@cs.umass.edu

  2. 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

  3. 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

  4. sea-critters

  5. 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

  6. 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.

  7. 1. go out to sea

  8. 2. collect samples

  9. 3. flowcam in action

  10. 4. zoom in

  11. 5. analyze output

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

  13. big picture

  14. segmentation • Step 1: Perform segmentation

  15. 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?

  16. 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

  17. 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

  18. 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!!

  19. 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)

  20. Thank You! Questions?

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