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Automatic In Situ Identification of Plankton
M. Blaschko, G. Holness, M. Mattar, D. Lisin, P. Utgoff, A. Hanson, H. Schultz, E. Riseman, M. Seraki, W. Balch, B. Tupper
Problem:
Motivation:
• Identify taxa of phytoplankton from images
taken in situ by FlowCAM
• Phytoplankton is the basis of the food chain for marine life
• Integral component of global carbon cycle
• Studying abundance of different species important for understanding of global and
local ecology
System Overview
• Manual identification is a daunting task, so automated solution is needed
Flow Cam
Raw Images
Feature
Extraction
Ground Truth Labeling
Feature
Vectors
Feature
Selection
Classification
Ensemble
I1
f11 f12
f11
C1
I2
MATLAB
and
C/C++
f21 f22
f22
Vote
C2
Class
Label
I3
f31 f32
f31
C3
Image Acquisition
Challenges
• FlowCAM produces thousands of images in short time
• Low magnification to increase field of view, resulting
in low resolution images
FFT
• Images contain any type of organism, i.e. not restricted
to any particular taxon
• FlowCAM developed at Bigelow Labs
• Power Spectra of ADIAC 100x images (top) and FlowCAM
4x images (bottom)
FFT
• Water siphoned directly from the ocean
• Particles exhibiting florescence are imaged
Features
Feature Space
• Cells categorized visually by shape and texture
Shape Features
Texture Features
•
•
•
•
(h2, w2)
h2
• Perimeter
• Gaussian Differential
• Area
• Co-occurrence
• Moments
• Local point features
(h1, w1)
h1
Coordinate system
Each point represents an instance
2D Example using height and width
Height and width are features
• Convexity
• Contour statistics
w2
w
1
• Contour spectrum
Classification
T
classifier
inducer
Classification Methods
ˆf ( x; )  yˆ
•
•
•
•
•
•
•
Instance: x1= <x11,…,x1d>
Class label: Yi є { c1,…,cK} class labels
Labeled instance: (xi, yi)
Training set: T= {(x1,y1),…,(xN,yN)}
Partition feature space into regions
Each region contains instances in a class
Classifier Induction: Estimate function
mapping instances to class labels
• Sometimes estimates commit errors
Single classifier Results
• K-Nearest Neighbors
Ensembles
T
• Decision Trees
• Naïve Bayes
T
CI1
CI2
fˆ1 ( x;1 )
ˆf ( x; )
2
2
• Combined estimates can lead to
increased accuracy
Vote
• Ridge Regression
• Support Vector Machines
T
CIM
Ensemble classifier Results
ˆf ( x; )
M
M
ŷ
• Improvements possible if
individual classifiers are
independent
• Methods used: Boosting, Bagging,
and Multi-Classifier
Conclusion
• Combinations of shape and texture
performed best
Experiments
• 980 expert labeled FlowCAM image
• pool of 780 total features
• 10-Fold Cross Validation
• Best results with Support Vector Machines
Best accuracy was 73%, comparable to
consistency rate of human experts
Future Work
• Automated Feature Selection
• Improved ensemble performance gains
by inducing classifier independence
• Experiments with Local image Features