u niversity of m assachusetts, a mherst department of computer science bigelow: plankton...
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UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS, A, AMHERST • MHERST • Department of Computer Science Department of Computer Science
Bigelow: Plankton Classification
CMPSCI: 570/670Spring 2006
Marwan (Moe) Mattar
www.cs.umass.edu/~mmattar
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS, A, AMHERST • MHERST • Department of Computer Science Department of Computer Science 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
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS, A, AMHERST • MHERST • Department of Computer Science Department of Computer Science 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
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS, A, AMHERST • MHERST • Department of Computer Science Department of Computer Science 4
sea-critters
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS, A, AMHERST • MHERST • Department of Computer Science Department of Computer Science 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
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS, A, AMHERST • MHERST • Department of Computer Science Department of Computer Science 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.
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS, A, AMHERST • MHERST • Department of Computer Science Department of Computer Science 7
1. go out to sea
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS, A, AMHERST • MHERST • Department of Computer Science Department of Computer Science 8
2. collect samples
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS, A, AMHERST • MHERST • Department of Computer Science Department of Computer Science 9
3. flowcam in action
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS, A, AMHERST • MHERST • Department of Computer Science Department of Computer Science 10
4. zoom in
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS, A, AMHERST • MHERST • Department of Computer Science Department of Computer Science 11
5. analyze output
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS, A, AMHERST • MHERST • Department of Computer Science Department of Computer Science 12
data set 982 training images belonging to 13 classes Initial set had many more images from a lot
more classes
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS, A, AMHERST • MHERST • Department of Computer Science Department of Computer Science 13
big picture
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS, A, AMHERST • MHERST • Department of Computer Science Department of Computer Science 14
segmentation Step 1: Perform segmentation
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS, A, AMHERST • MHERST • Department of Computer Science Department of Computer Science 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?
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS, A, AMHERST • MHERST • Department of Computer Science Department of Computer Science 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
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS, A, AMHERST • MHERST • Department of Computer Science Department of Computer Science 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
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS, A, AMHERST • MHERST • Department of Computer Science Department of Computer Science 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!!
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS, A, AMHERST • MHERST • Department of Computer Science Department of Computer Science 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)
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS, A, AMHERST • MHERST • Department of Computer Science Department of Computer Science 20
Thank You!
Questions?