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Research map / Learning theory and algorithms / Clustering

Compassionately Conservative Balanced Cuts for Image Segmentation

CVPR 2018 · Learning theory and algorithms › Clustering · approach: Clustering approximation algorithms · 4 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use CCN1-H instead of NCut-HHierarchical segmentation · BSDS500 test, Lab affinity, fixedCovering: 0.51 -> 0.57Helped
Use CCN1-H instead of NCut-HHierarchical segmentation · BSDS500 test, Lab affinity, fixedPRI: 0.78 -> 0.69Hurt
Use CCN1-H instead of NCut-HHierarchical segmentation · BSDS500 test, Lab affinity, fixedVI: 1.98 -> 1.81Helped
Use CCN1-H instead of NCut-HHierarchical segmentation · BSDS500 test, mPb affinity, fixedCovering: 0.30 -> 0.38Helped
Use CCN1-H instead of NCut-HHierarchical segmentation · BSDS500 test, mPb affinity, fixedPRI: 0.83 -> 0.73Hurt
Use CCN1-H instead of NCut-HHierarchical segmentation · BSDS500 test, mPb affinity, fixedVI: 2.61 -> 2.35Helped
Use CCN1-H instead of NCut-HHierarchical segmentation · BSDS500 test, PMI affinity, fixedCovering: 0.37 -> 0.47Helped
Use CCN1-H instead of NCut-HHierarchical segmentation · BSDS500 test, PMI affinity, fixedPRI: 0.87 -> 0.78Hurt
Use CCN1-H instead of NCut-HHierarchical segmentation · BSDS500 test, PMI affinity, fixedVI: 2.37 -> 1.99Helped
Set the CCB parameter to smaller values than τ=1 (ablation)CCB segmentation · BSDS500 testsegmentation performanceHurt
Use hierarchical 2-way segmentation instead of simultaneous multiway segmentationCCB segmentation · BSDS500 testsegmentation performanceHelped

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