U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science Bigelow: Plankton Classification CMPSCI: 570/670 Spring 2006 Marwan (Moe) Mattar

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

[email protected]

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


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


sea-critters


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


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.


1. go out to sea


2. collect samples


3. flowcam in action


4. zoom in


5. analyze output


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

more classes


big picture


segmentation Step 1: Perform segmentation


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?


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


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


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!!


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)


Thank You!

Questions?

Documents

U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science Bigelow: Plankton Classification CMPSCI: 570/670 Spring 2006 Marwan (Moe) Mattar