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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 { 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