Automatic classification of plankton from digital images
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Features. Shape Features Perimeter, Area, Moments, Contour, Convexity, Symmetry. Texture Features Gaussian Differential Co-occurrence Local point features. Sample. Ensemble Classifier Results.

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Automatic Classification of Plankton from Digital Images

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Shape Features

Perimeter, Area,

Moments, Contour,

Convexity, Symmetry

Texture Features

Gaussian Differential


Local point features


Ensemble Classifier Results

M Sieracki1, E Riseman2, W Balch1, M Benfield3, A Hanson2, C Pilskaln1, H Schultz2, C Sieracki4, P Utgoff2, M Blaschko2, G Holness2, M Mattar2, D Lisin2, B Tupper1

Bigelow Laboratory for Ocean Science1 Computer Vision Lab, U. Mass. Amherst2 Louisiana State University3Fluid Imaging Technologies4

Automatic Classification of Plankton from Digital Images

Marine particles, including plankton and non-living particles, play important roles in ecosystem function and material flux in the oceans. Digital imaging technology used in instruments to study these particles can rapidly produce huge archives of images that require expert interpretation. Automated methods to assist the expert interpret these images are urgently needed. We are building automatic classifier systems to work with the experts to efficiently and accurately classify images of marine particles. We will use images from in-situ camera instruments (e.g. VPR) for zooplankton and marine snow, an imaging-in-flow system (FlowCAM) for phytoplankton, and digital fluorescence microscopy for pico- and nanoplankton. Experiments were conducted using low resolution FlowCAM images of 13 classes of phytoplankton from natural communities, and a variety of image features and classifiers, including classifier ensembles. These preliminary tests yielded classification accuracy of over 70%, compared to published human expert agreement of about 80%. This indicates that automated classification will be practical to automate the majority of images. We intend to develop a probabilistic approach to particle enumeration, and to test the generality of our classifiers across instrument types.

Classification Methods

K-Nearest Neighbors

Decision Trees

Naïve Bayes

Ridge Regression

Support Vector Machines

Expert Classified

Image Sets

1 Video Plankton Recorder (VPR)

Test Image Sets

Label 1

Experts manually

classify particles

2 FlowCAM Imaging-in-flow

Label 2

3 Epifluorescence Microscopy

Label 3

  • Conclusions

  • Combinations of shape and texture features performed best

  • Support Vector Machine classifier performed best

  • Best accuracy was 73%, approaching consistency rate of human experts (80%)

Preliminary Results for FlowCAM Images

  • Experiments

  • 980 expert labeled FlowCAM images

  • 780 total features

  • 5 classifiers used

  • Future Work

  • Apply to other image types, more expert classified image sets

  • Automated feature selection

  • 3D FlowCAM (dual aspect angle images)

  • Experiments with local image features

  • Software tools for experts

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