Boundary Extraction in Natural Images Using Ultrametric Contour Maps

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Boundary Extraction in Natural Images Using Ultrametric Contour Maps Pablo Arbeláez Université Paris Dauphine Presented by Derek Hoiem

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Boundary Extraction in Natural Images Using Ultrametric Contour Maps. Pablo Arbel á ez Universit é Paris Dauphine Presented by Derek Hoiem. What is segmentation?. What is segmentation?. Segmentation is a result. Face. Woman. What is segmentation?. Segmentation is a result - PowerPoint PPT Presentation

Transcript of Boundary Extraction in Natural Images Using Ultrametric Contour Maps

Page 1: Boundary Extraction in Natural Images Using Ultrametric Contour Maps

Boundary Extraction in Natural Images Using Ultrametric Contour Maps

Pablo Arbeláez Université Paris Dauphine

Presented byDerek Hoiem

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What is segmentation?

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What is segmentation?

• Segmentation is a result

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What is segmentation?

• Segmentation is a result

• Segmentation is a process

Woman

Face

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What is segmentation?

• Segmentation is a result

• Segmentation is a process

• Segmentation is a guide

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Segmentation as a Guide

• Multiple Segmentations

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Segmentation as a Guide

• Multiple Segmentations

• Hierarchy of Segmentations

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Key Concepts/Contributions

• Hierarchical segmentation by iterative merging

• Ultrametric dissimilarities

• Thorough evaluation on BSDS

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Hierarchical Segmentation

λ

3 Region Image Dendrogram

Contour Image

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Ultrametric Contour Map

• Ultrametric– Definition: D(x,y) <= max{ D(x,z), D(z,y) }

The union R12 of two regions R1 and R2 must have >= distance to adjacent region R3 than either R1 or R2

λ

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Ultrametric Contour Map

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Region Dissimilarity

1. Dc(R1, R2): mean boundary contrast– contrast(x) = max L*a*b* diff within radius of x

2. Dg(R1, R2): mean boundary gradient– gradient(x) = Pb(x)

3. Da(R1): Area + α3 Scatter (in color space)

D(R1, R2) = [Dc(R1, R2) + α1 Dg(R1, R2)] · min{ Da(R1) , Da(R2) }α2

Learned Parameters: xi = 4.5 α1 = 5 α2 = 0.2 α3 = 0

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Examples

Contrast

Contrast + Gradient

Contrast + Gradient + Region

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Algorithm Summary

• Create Initial Contours:– Extrema in gray channel form regions– Assign pixels to regions based on above

ultrametric

• Iteratively merge regions– Keep adjacency/distance matrix

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Comparison

• Martin et al. (Pb)• Canny edge detector• Hierarchical watersheds (using MFM for gradient)

[Najman and Schmitt 1996]• Variational (global energy minimization)

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Pb

No Boundary

Boundary

[Martin Fowlkes Malik 2004]

Oriented Edges

Brightness Gradient

Color Gradient

Texture Gradient

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Pb

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Variational Method

[Koepfler Lopez Morel 1994]

Originally Wavelet-based Textons

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Comparison• MFM: Martin et al. (Pb)• Canny: Canny edge detector• WS: Hierarchical watersheds (using MFM for gradient) [Najman and Schmitt 1996]• MS: Variational (global energy minimization)

Edge-Based Region-Based

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Comparison

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Results

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Results

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Best Results

http://www.ceremade.dauphine.fr/~arbelaez/results-UCM/main.html

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Best Results

http://www.ceremade.dauphine.fr/~arbelaez/results-UCM/main.html

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Best Results

http://www.ceremade.dauphine.fr/~arbelaez/results-UCM/main.html

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Best Results

http://www.ceremade.dauphine.fr/~arbelaez/results-UCM/main.html

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Median Results

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Median Results

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Median Results

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Median Results

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Worst Results

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Worst Results

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Worst Results

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Worst Results

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Hierarchies vs. Multiple Segmentations

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Revising Segmentation