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1 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 2
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Introduction Original ImageContour Contour 3
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Introduction Original ImageSegmentation Segmentation 4
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Introduction From Contour to Segmentation Original ImageSegmentationContour 5
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Introduction Goal Contour Detection Hierarchical Segmentation from Contours Original ImageSegmentationContour 6
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Outline Introduction Contour Detection Hierarchical Segmentation Results Conclusion 7
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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 8
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Learn local boundary cues Image Local Boundary Cues Model Brightness Color Texture Cue Combination Contour Detection 9
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Learn local boundary cues Brightness L*a*b* colorspace Color L*a*b* colorspace Texture Convolve with 17 filters Filters for creating textons 10 Contour Detection
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11 Learn local boundary cues Oriented gradient of histograms Example Gradient magnitude G at location(x, y) Three scales of r 11 Contour Detection ure
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12 Learn local boundary cues Local Cues Combination 12 Contour Detection ure
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Global framework to capture closure, continuity Contour Detection 13 V:image pixels E:connections between pairs of nearby pixels =>Build a weighted graph G=(V,E) from image
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Global framework to capture closure, continuity Contour Detection 14
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Local Cues and global cues combination Contour Detection 15 Local CuesGlobal cues
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Outline Introduction Contour Detection Hierarchical Segmentation Results Conclusion 16
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Hierarchical Segmentation Multiple Segmentations Fixed resolution Hierarchy of Segmentations Flexible resolution adjustment 17
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Hierarchical Segmentation 1. From contours to segmentation 2. Hierarchical segmentation by iterative merging 18
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Hierarchical Segmentation From contours to segmentation Watershed Transform Concept 19
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Hierarchical Segmentation From contours to segmentation Watershed Transform Example 20
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Hierarchical Segmentation From contours to segmentation Watershed Transform 21 Boundary strength Artifacts Weight each arc
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Hierarchical Segmentation From contours to segmentation Oriented Watershed Transform 22 WT OWT
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Hierarchical Segmentation Hierarchical segmentation by iterative merging Hierarchical segmentation Example 23
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Brief Summary 24 Original Image - Local cues - Global cues Oriented Gradient of histograms Contour Oriented Watershed Transform Iterative Merging Hierarchical Segmentation
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Outline Introduction Contour Detection Hierarchical Segmentation Results Conclusion 25
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Result 26
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Result 27
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Result 28 Evaluation of segmentation algorithmsEvaluation of contour detector BSDS300 Dataset
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Outline Introduction Contour Detection Hierarchical Segmentation Results Conclusion 29
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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 30
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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. 31
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Outline Introduction Contour Detection Hierarchical Segmentation Evaluation Results 32
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Boundary Benchmarks ODS : optimal dataset scale OIS : optimal image scale AP :average precision 33
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Region benchmarks(1) Segment Covering Probabilistic Rand Index [Unnikrishnan et. al. 07] [Yang et. al. 08] Variation of Information [Meila 05] Distance Between two segmentations in terms of their average conditional entropy given by 34
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Region benchmarks(2) CoveringRand Index Variation of Information 35
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Additional Dataset 36
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