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

U NIVERSITY OF M ASSACHUSETTS, A MHERST 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

U NIVERSITY OF M ASSACHUSETTS, A MHERST 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

U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 4 sea-critters

U NIVERSITY OF M ASSACHUSETTS, A MHERST 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

U NIVERSITY OF M ASSACHUSETTS, A MHERST 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.

U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 7 1. go out to sea

U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 8 2. collect samples

U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 9 3. flowcam in action

U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science zoom in

U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science analyze output

U NIVERSITY OF M ASSACHUSETTS, A MHERST 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

U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 13 big picture

U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 14 segmentation Step 1: Perform segmentation

U NIVERSITY OF M ASSACHUSETTS, A MHERST 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?

U NIVERSITY OF M ASSACHUSETTS, A MHERST 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 2 nd degree polynomial performed better than a linear classifier 3 rd degree polynomial over-fit Overall best result: 66% using 21 features

U NIVERSITY OF M ASSACHUSETTS, A MHERST 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

U NIVERSITY OF M ASSACHUSETTS, A MHERST 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!!

U NIVERSITY OF M ASSACHUSETTS, A MHERST 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)

U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 20 Thank You! Questions?