Automatic In Situ Identification of Plankton

1. T CI1 Vote T CI2 (h2, w2) h2 classifier inducer (h1, w1) T h1 T CIM w2 w1 Flow Cam Raw Images Image Acquisition Ground Truth Labeling Challenges FFT • FlowCAM produces thousands of images in short time • Low magnification to increase field of view, resulting in low resolution images • Images contain any type of organism, i.e. not restricted to any particular taxon • Power Spectra of ADIAC 100x images (top) and FlowCAM 4x images (bottom) FeatureExtraction FeatureVectors FeatureSelection Classification Ensemble • FlowCAM developed at Bigelow Labs • Water siphoned directly from the ocean • Particles exhibiting florescence are imaged FFT I1 MATLAB and C/C++ f11 f12 f11 C1 I2 f21 f22 f22 C2 ClassLabel Vote • Features • Cells categorized visually by shape and texture Feature Space I3 f31 f32 f31 C3 • Coordinate system • Each point represents an instance • 2D Example using height and width • Height and width are features • Shape Features • Perimeter • Area • Moments • Convexity • Contour statistics • Contour spectrum • Texture Features • Gaussian Differential • Co-occurrence • Local point features Ensemble classifier Results 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 • Motivation: • 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 • Manual identification is a daunting task, so automated solution is needed • Problem: • Identify taxa of phytoplankton from images taken in situ by FlowCAM System Overview Classification • Classification Methods • K-Nearest Neighbors • Decision Trees • Naïve Bayes • Ridge Regression • Support Vector Machines Ensembles • Combined estimates can lead to increased accuracy • Improvements possible if individual classifiers are independent • Methods used: Boosting, Bagging, and Multi-Classifier • Instance: x1= <x11,…,x1d> • Class label: Yiє { 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 functionmapping instances to class labels • Sometimes estimates commit errors Single classifier Results • Conclusion • Combinations of shape and texture performed best • Best results with Support Vector Machines Best accuracy was 73%, comparable to consistency rate of human experts • Experiments • 980 expert labeled FlowCAM image • pool of 780 total features • 10-Fold Cross Validation • Future Work • Automated Feature Selection • Improved ensemble performance gains by inducing classifier independence • Experiments with Local image Features