Recognition using Regions (Demo)

Sudheendra V

Outline

• Generating multiple segmentations– Normalized cuts [Ren & Malik (2003)]

• Uniform regions– Watershed transform [Arbel´aez1et al. (2009)]

• Non-uniform• Multiple scales

• Discovering object regions using LDA– ground truth segments– multiple segmentation– single segmentation– hierarchical segmentation

Multiple Segmentations

• Normalized cuts– segmentation as graph partitioning

• nodes -> pixels, edge between neighboring pixels

• edge weight -> affinity between pixels• partition graph into K components

– parameters• number of partitions K

– properties• similar sized partitions (normalized)• preserves region boundaries for large enough

K– multiple segmentations

• vary number of partitions K• resize image to different resolutions

http://www.cs.sfu.ca/~mori/research/superpixels/

affinity matrix

Normalized cuts (examples)

K = 4

K = 6

K = 7

Normalized cuts (examples)

K = 3

K = 5

K = 7

Extra edge •“normalized” regions

Normalized cuts (examples)

K = 3

K = 5

K = 7

Multiple Segmentations• Watershed transform

– contours are detected using texture, edge cues and oriented watershed transform used to determine contour scale

– parameters• thresholding scale for contours, k

– properties• variable sized regions• preserves region boundaries

– multiple segmentations• vary thresholding scale k• resize image to different resolutions

http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/

Contours at multiple scales Threshold at a scale

Watershed (examples)

K = 200

K = 180

K = 160

Threshold at different contour scales K to generate multiple segmentations

Watershed (examples)

K = 195

K = 183

K = 169

Threshold at different contour scales K to generate multiple segmentations

Watershed (Hierarchical segmentation)

Thresholding scales in an increasing sequence produces a hierarchical segmentation

K = 175

K = 155

K = 190

K = 140

Watershed (Hierarchical segmentation)

Thresholding scales in an increasing sequence produces a hierarchical segmentation

K = 175

K = 155

K = 200

K = 145

Ncuts vs Watershed Ncuts

Watershed

Comparison of multiple segmentations generated using Ncuts vs Watershed

Ncuts vs Watershed Ncuts

Watershed

Comparison of multiple segmentations generated using Ncuts vs Watershed

Outline

• Generating multiple segmentations– Normalized cuts [Ren & Malik (2003)]

• Uniform regions– Watershed transform [Arbel´aez1et al. (2009)]

• Non-uniform• Multiple scales

• Discovering object regions using LDA– ground truth segments– multiple segmentation– single segmentation– hierarchical segmentation

Multiple Segmentations

Discovering object regions using LDA

• Approach

• Parameters – number of topics to discover

Generate multiple segmentations

Extract local features (SIFT)

Bag of words rep for each

segment

Use LDA to discover topics

based on word co-occurrence

Rank segments based on

similarity to topic

Dataset

Note that the paper uses a larger set containing ~ 4000 images (MSRC_v0)

MSRC_v2 dataset

• 23 categories

• 591 images

• 1648 objects

Distribution of categories in MSRC_v2

Implementation details

– Dense sift on edge points and 3 different scales

– 2000 visual words– 8 segmentations using different parameters– ~ 40k segments in total– LDA takes ~ 10 mins

Ground Truth Segments

• ground truth segments are directly used• number of topics set to 25 (~ num categories)

Top 20 segments in terms of similarity of word distribution to a topic

Ground Truth SegmentsNumber of topics = 25

Ground Truth SegmentsNumber of topics = 50

Ground Truth SegmentsNumber of topics = 75

• Quantitative results

Overlap score on top 20 segments

Average precision (area under precision-recall curve)

Ground Truth Segments

Multiple segmentations• Normalized cuts

– k = {3, 5, 7, 9}– 2 resolutions

Multiple segmentations

Multiple segmentations

Multiple segmentations vs. Ground truth

• Quantitative results

Overlap score on top 20 segments

Average precision (area under precision-recall curve)

Multiple segmentations

Multiple segmentations• Effect of number of images

Overlap score on top 20 segments

• topics are easier to discover with more object instances

Single segmentation

Multiple segmentations vs. Single segmentation

Single segmentation returns partial objects for some classes

Single segmentation

Multiple segmentations vs. Single segmentation

Single segmentation returns partial objects for some classes

Single segmentation

Multiple segmentations vs. Single segmentation

• Quantitative results

Overlap score on top 20 segments

Average precision (area under precision-recall curve)

Single/Multiple segmentations

Hierarchical segmentationHierarchical segmentation vs. Multiple segmentation (Ncuts)

Watershed threshold set such there are 12 leaf nodes and entire hierarchical tree is used by LDA

Hierarchical segmentation

Hierarchical segmentation vs. Multiple segmentation (Ncuts)

Hierarchical segmentationHierarchical segmentation vs. Multiple segmentation (Ncuts)

• Quantitative results

Overlap score on top 20 segments

Hierarchical segmentation

Contour-based watershed method

• does better for objects with few internal contours (grass, sky)

• is worse for objects with large number of contours (flower, airplane)

Conclusion• Generating multiple segmentations

– ncuts and watershed provide different tradeoffs– bottom-up segmentation needs different parameters

for different objects

• Discovering objects using LDA– number of topics matters quite a bit– topics are easier to discover with more examples– multiple segmentation does better than because

different objects require different parameters – contour-based watershed method does better for

objects with few internal contours