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Coastal Image ClassificationBas Hoonhout, Max Radermacher, Fedor Baart

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Coastal Image Classification

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Why is it useful?

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How does it work?

1. Segmentation (superpixels)Create clusters of pixels with similar intrinsic properties

2. Feature extractionExtract as much information as possible from superpixels

3. Model construction and trainingTrain a model to discriminate between classes using features

4. Model predictionPredict classification of an unseen image

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Step 1: segmentation (superpixels)

Intensity: R, G, B, C, Y, M, K, …Position: N, M… and gradients and filters

Intensity, position and gradientsShape, texture, …Variance, frequency, …

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Step 2: feature extraction

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Step 2: feature extraction16 channels and1727 featuresper superpixel

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Step 3: model construction and training

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Step 3: model construction and training

C00

f1 … fK

…

f1 … fK

C0M

f1 … fK

…

f1 … fK

…

f1 … fK

…

f1 … fK

CN0

f1 … fK

…

f1 … fK

CNM

f1 … fK

C00

f1 … fK

…

f1 … fK

C0M

f1 … fK

…

f1 … fK

…

f1 … fK

…

f1 … fK

CN0

f1 … fK

…

f1 … fK

CNM

f1 … fK

Ψ01 Ψ0M

… …

ΨN1 ΨNM

Ψ10 … Ψ1M

ΨN0 ... ΨNM

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Step 3: model construction and training

pixel intensity

sea beach

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Step 3: model construction and training

hue

satu

rati

on

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Step 3: model construction and training

et cetera, until we have a 1727-dimensional feature space

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Step 4: model prediction

hue

satu

rati

on

sea

beach

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Performance

• Ongoing research!

• 192 manually annotated Argus imagesdoi:10.4121/uuid:08400507-4731-4cb2-a7ec-9ed2937db119

• Training set 75%, test set 25%

• Last benchmark test: >90% correct

• Target benchmark test: >95% correct

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Take home messages

Classify coastal images?Spend your creativity on features!

Classify large datasets?Go for full automation!

What would you do with 95%accurate, automated classificationof coastal images?

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