1 / 20

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

shaine-sanders
Download Presentation

Bigelow: Plankton Classification

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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?

More Related