CONTOUR DETECTION AND
HIERARCHICAL IMAGE SEGMENTATION
P. Arbelaez, M. Maire, C. Fowlkes, J. Malik. Contour Detection and Hierarchical
image Segmentation. IEEE Trans. on PAMI , 2011.
Student: Hsin-Min Cheng
Advisor: Sheng-Jyh Wang
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Outline
 Introduction
 Contour Detection
 Hierarchical Segmentation
 Results
 Conclusion
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Introduction
 Contour
Original Image
Contour
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Introduction
 Segmentation
Original Image
Segmentation
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Introduction
 From Contour to Segmentation
Original Image
Contour
Segmentation
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Introduction
 Goal
 Contour Detection
 Hierarchical Segmentation from Contours
Original Image
Contour
Segmentation
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Outline
 Introduction
 Contour Detection
 Hierarchical Segmentation
 Results
 Conclusion
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Contour Detection
1. Learn local boundary cues
2. Global framework to capture
closure, continuity
3. Local Cues and global cues
combination
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Contour Detection
 Learn local boundary cues
Image
Local Boundary Cues
Brightness
Color
Cue Combination
Model
Texture
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Contour Detection
 Learn local boundary cues
 Brightness
 L*a*b* colorspace
 Color
 L*a*b* colorspace
 Texture
 Convolve with 17 filters
Filters for creating textons
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Contour Detection
 Learn local boundary cues
 Oriented gradient of histograms
 Example
 Gradient magnitude G at location(x, y)
 Three scales of r
ure[ ,  , 2 ]
2
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Contour Detection
 Learn local boundary cues
 Local Cues Combination

r  [ ,  , 2 ]
ure
2
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Contour Detection
 Global framework to capture closure, continuity
V: image pixels
E: connections between pairs of nearby pixels
=>Build a weighted graph G=(V,E) from image
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Contour Detection
 Global framework to capture closure, continuity
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Contour Detection
 Local Cues and global cues combination
Local Cues
Global cues
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Outline
 Introduction
 Contour Detection
 Hierarchical Segmentation
 Results
 Conclusion
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Hierarchical Segmentation
 Multiple Segmentations
 Fixed resolution
 Hierarchy of Segmentations
 Flexible resolution adjustment
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Hierarchical Segmentation
1. From contours to segmentation
2. Hierarchical segmentation by
iterative merging
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Hierarchical Segmentation
 From contours to segmentation
 Watershed Transform
 Concept
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Hierarchical Segmentation
 From contours to segmentation
 Watershed Transform
 Example
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Hierarchical Segmentation
 From contours to segmentation
 Watershed Transform
Artifacts
Boundary strength
Weight each arc
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Hierarchical Segmentation
 From contours to segmentation
 Oriented Watershed Transform
OWT
WT
Hierarchical Segmentation
 Hierarchical segmentation by iterative
merging
 Hierarchical segmentation
 Example
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Brief Summary
Original Image
- Local cues
- Global cues
Oriented Gradient
of histograms
Contour
Oriented Watershed Transform
Hierarchical Segmentation
Iterative Merging
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Outline
 Introduction
 Contour Detection
 Hierarchical Segmentation
 Results
 Conclusion
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Result
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Result
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Result
 BSDS300 Dataset
Evaluation of contour detector
Evaluation of segmentation algorithms
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Outline
 Introduction
 Contour Detection
 Hierarchical Segmentation
 Results
 Conclusion
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Conclusion
 A high performance contour detector, combining
local and global image information
 A method to transform any contour detector signal
into a hierarchy of regions while preserving
contour quality
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Reference
 P. Arbelaez, M. Maire, C. Fowlkes and J. Malik. Contour Detection
and Hierarchical Image Segmentation. IEEE TPAMI, Vol. 33, No. 5,
pp. 898-916, May 2011
 P. Arbelaez, M. Maire, C. Fowlkes and J. Malik. From Contours to
Regions: An Empirical Evaluation. In CVPR 2009.
 P. Arbelaez and L. Cohen. Constrained Image Segmentation from
Hierarchical Boundaries. In CVPR 2008.
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