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How do ideas from perceptual

organization relate to natural

scenes?

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Brunswik & Kamiya 1953

• Thesis: Gestalt rules reflect the structure of the natural world

• Attempted to validate the grouping rule of proximity of similars

• Brunswik was ahead of his time… we now have the tools. Egon Brunswik (1903-1955)

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• Can we define these cues for real images?• Are these cues “ecologically valid”?• How informative are different cues?

Grouping Figure/Ground

Ecological Statistics of Perceptual Organization

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• Task: detect generic pattern or group

• Signal: class of patterns, known null hypothesis

• Cues: optimal test is usually obvious

• Result: mathematically precise characterization of when detection is possible

• Task: capture “useful” information about the scene

• Signal: natural image statistics, clutter

• Cues: something computable from real pixels

• Result: empirical statistics about relative power of different cues

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Berkeley Segmentation DataSet [BSDS]

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Cues:a) distance [proximity]b) region cues [similarity]c) boundary cues [connectedness, closure, convexity]

What image measurements allow us to gauge the probability that pixels i and j belong to the same group?

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Learning Pairwise AffinitiesSij – indicator variable as to whether pixels i and j were marked as belonging to the same group by human

subjects.

Wij – our estimate of the likelihood that pixel i and j belong to the same group conditioned on the image measurements.

• Use the ground truth given by human segmentations to calibrate cues.• Learn “statistically optimal” cue combination in a supervised learning framework• Ecological Statistics: Measure the relative power of different cues for natural scenes

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Color

a*b*

Brightness

L*

TextureOriginal Image

Wij

Distance

ED

2

Boundary Processing

Textons

A

B

C

A

B

C

2

Region Processing

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Evaluation Measures

1. Precision-Recall of same-segment pairs– Precision is P(Sij=1 | Wij > t)

– Recall is P(Wij > t | Sij = 1)

2. Mutual Information between W and S

Groundtruth SijEstimate Wij

∫ p(s,w) log [p(s)p(w) / p(s,w)]

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Individual Features

Patches Gradients

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Affinity Model vs. Human Segmentation

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Findings

• Both Edges and Patches provide useful “independent” information.

• Texture gradients can be quite powerful

• Color patches better than gradients

• Brightness gradients better than patches.

• Proximity is a result, not a cause of grouping

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Figure-Ground Labeling

- start with 200 segmented images of natural scenes- boundaries labeled by at least 2 different human subjects- subjects agree on 88% of contours labeled

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Local Cues for Figure/Ground

• Assume we have a perfect segmentation

• Can we predict which region a contour belongs to based on it’s local shape?– Size/Surroundedness– Convexity– Lower Region

15Size(p) = log(AreaF / AreaG)

Size and Surroundedness [Rubin 1921]

GFp

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Convexity(p) = log(ConvF / ConvG)

ConvG = percentage of straight lines that lie completely within region G

pG F

Convexity [Metzger 1953, Kanizsa and Gerbino 1976]

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LowerRegion(p) = θG

Lower Region[Vecera, Vogel & Woodman 2002]

θ

p

center of mass

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Figural regions tend to be convex

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Figural regions tend to lie below ground regions

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Size

LowerRegion

Convexity

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Power of cue depends on support of the analysis window.

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Power of cue depends on support of the analysis window.

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“Upper Bounding” Local Performance

• Present human subjects with local shapes, seen through an aperture.

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Human Performance on Local Figure-Ground

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Extension to Real Images

• Build up library of prototypical contour configurations by clustering local shape descriptors– Geometric Blur [Berg & Malik 01]

• Train a classifier which uses similarities to these prototype shapes to predict figure/ground label

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Shapemes

Classifier using 64 shapeme features: 61%

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Globalization of Figure/Ground Measurements

• Averaging local shapeme cue over human-marked boundaries: 71%

• Prior over junction types and label continuity: 79%