Object Segmentation

Object Segmentation

Presented by

Sherin Aly

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What is a ‘Good Segmentation’?

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

Learning a classification model for segmentation

Xiaofeng Ren and Jitendra Malik

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methodology

• Two-class classification model

• Over segmentation as preprocessing

• They use classical Gestalt cues– Contour, texture, brightness and

continuation

• A linear classifier is used for training

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Good Vs Bad segmentation

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a) Image from Corel Imagebase

b) superimposed with a human markedsegmentation

c) Same image with Bad segmentation

How do we distinguish good segmentations from bad

segmentations?

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How?

• Use “Classical Gestalt cues”– proximity, similarity and good continuation

• Instead of Ad-hoc decision about features combination

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Gestalt Principles of Grouping

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http://allpsych.com/psychology101/perception.html

In order to interpret what we receive through our senses,we attempt to organize this information into certain groups.

Methodology

• Preprocessing

• Feature extraction

• Feature evaluation

• Training

• Optimization

• Find good segmentaion

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Preprocessing

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Superpixel mapK=200

Reconstruction of human segmentation from Superpixels

a contour-based measure is used to quantify this approximation

•Local•Coherent•Preserve structure

•Contour •texture

12 The percentage of human marked boundaries covered by the superpixel maps

Tolerance 1,2,and 3

Feature Extraction

1. inter-region texture similarity

2. intra-region texture similarity

3. inter-region brightness similarity

4. intra-region brightness similarity

5. inter-region contour energy

6. intra-region contour energy

7. curvilinear continuity 13

Feature Extraction








Feature Extraction








Power of Gestalt cues

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=

Training the classifier

• simple logistic regression classifier,

17Empirical distribution of pairs of features

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Precision is the fraction of detections which are true positives. Recall is the fraction of true positives which are detected

Conclusion

• There simple linear classifier had promising results on a variety of natural images.

• boundary contour is the most informative grouping cue, and it is in essence discriminative.

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Pros & Cons

• Cons– The larger spatial support that superpixels

provide, allowing more global features to be computed than on pixels alone.

– The use of superpixels improves the computational efficiency

– SuperPixels technique is very applicable

• Pros– Might fall in Local Minima

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Combining Top-down and Bottom-up Segmentation

Eran Borenstein

Eitan Sharon

Shimon Ullman

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Motivation

• Bottom-Up segmentation– Rely on continuity principle– Capture image properties “texture, grey level uniformity

and contour continuity”– Segmentation based on similarities between image

regions

• How can we capture prior knowledge of a specific object (class)?– Answer: Top-Down Segmentation– use prior knowledge about an object

Credit: Joseph Djugash

Bottom-Up Segmentation

Slides from Eitan Sharon, “Segmentation and Boundary Detection Using Multiscale Intensity Measurements”.


Normalized-Cut Measure

Slides from Eitan Sharon, “Segmentation and Boundary Detection Using Multiscale Intensity Measurements”.Credit: Joseph Djugash

Top-Down approachInput Fragments

Matching CoverCredit: Joseph Djugash

Another step towards the middle

Bottom-Up

Top-Down


Some Definitions & Constraints

• Measure of saliency h(Γi), hi є [0,1)

• A configuration vector s contains labels si (1/-1) of all the segments (Si) in the tree

• The label si can be different from its parent’s label s i

–

• Cost function for a given s

Top-down term Bottom-up termDefines the weighted edge between Si & Si

–

Classification Costs

• The terminal segments of the tree determine the final classification

• The top-down term is defined as:

• The saliency of a segment should restrict its label (based on its parent’s label)

• The bottom-up term is defined as:

Confidence Map

• Evaluating the confidence of a region:

• Causes of Uncertainty of Classification– Bottom-up uncertainty – regions where there is no

salient bottom-up segment matching the top-down classification

– Top-down uncertainty – regions where the top-down classification is ambiguous (highly variable shape regions)

• The type of uncertainty and the confidence values can be used to select appropriate additional processing to improve segmentation

Results

• Calculate average distance between a given segmentation contour and a benchmark contour.

• Removing from the average all contour points having a confidence measure less than 0.1.

• The resulting confidence map efficiently separated regions of high and low consistency.

• The combined scheme improved the top-down contour by over 67% on average.

• This improvement was even larger in object parts with highly variable shape.

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Results (cont.)

•top-down process may produce a figure-ground approximation that does not follow the image discontinuities.•Salient bottom-up segments can correct these errors and delineate precise region boundaries

Buttom up

The initial classificationmap T(x, y)

Results III (cont.)

Results III (cont.)

the top-down completely misses a part of the object . The confidence map may be helpful in identifying such cases,

Results III (cont.)

bottom-up segmentation may be insufficient in detecting the figure-ground contour, and the top-down process completes the missing information

Results III (cont.)

Results III (cont.)

Salient bottom-up segments can correct these errors and delineateprecise region boundaries

Conclusion

• Buttom-up and top-down merits• Provide reliable confidence map• It take into account all discontinuities at all

scales

But:• If the object is assigned a given category, the

specific features cannot be adopted for other categories

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Constrained Parametric Min-Cuts for Automatic Object

SegmentationJoao Carreira

Cristian Sminchisescu

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Traditional Segmentation: Finding Homogeneous Regions

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gPb-owt-ucm: P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik. PAMI 2010.

Conventional Bottom-up Segmentation

Proposed approach

1. Split multiple times

2. Retain object-like segmentations

Bottom-up Object Segmentation

Credit: J. Carreira

High redundancy

Bottom-up Object Segmentation

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Credit: J. Carreira

A single multi-region segmentation or a hierarchy

Proposed Bottom-up Object Segmentation

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Credit: J. Carreira

single-shot multi-region segmentation

robust set of overlapping figure-ground segmentations

Segments with object-like regularitiessuperpixels

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Constrained Parametric Min-Cuts for Automatic Object Segmentation

Credit: J. Carreira

parametric max-flow solver

Figure ground segmentation by growing regions around seeds

Ranking

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Credit: J. Carreira

Initialization

• Foreground– Regular 5x5 grid geometry– Centroids of large N-Cuts regions– Centroids of superpixels closest to grid positions

• Background– Full image boundary– Horizontal boundaries– Vertical boundaries– All boundaries excluding the bottom one

Performance broadly invariant to different initializations

Generating a segment pool:constrained min-cut

min cuthard constraint

background

object

hard constraint

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Credit: J. Carreira

Generating a Segment Pool:Constrained Parametric Min-Cuts

background

object

hard constraint

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Credit: J. Carreira

background

object

hard constraint

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Credit: J. Carreira

background

object

hard constraint

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Credit: J. Carreira

background

object

hard constraint

background

object

hard constraint

Can solve for all values of object bias in the same time complexity of solving a single min-cut using a parametric max-flow solver

background

object

hard constraint

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Credit: J. Carreira

Fast Rejection

Large set of initial segmentations (~5500)

High Energy Low Energy

~2000 segments with the lowest energy

Cluster segments based on spatial overlap (at least 0.95)

Lowest energy member of each cluster (~154 in PASCAL VOC)

Credit:SasiKanth BendapudiYogeshwar Nagaraj

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Credit: J. Carreira

•ranks all the sampled object segmentations•discard all but a small subset of confident ones.

Ranking object hypotheses

mid-level, category independent features Boundary – normalized boundary energy Region – location, perimeter, area, Euler

number, orientation, contrast with background Gestalt – convexity, smoothness

GoodLow boundary energy

Non smooth.

High Euler number

High boundary energy

Smooth.

Euler number = 0

Bad

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Credit: J. Carreira

Segment Ranking

• Model data using a host of features– Graph partition properties– Region properties– Gestalt properties

• Apply Features Normalization• Train regressor with the largest overlap

ground-truth segment using Random Forests

• Diversify similar rankings using Maximal Marginal Relevance (MMR)

Graph Partition Properties

• Cut – Sum of affinities along segment boundary

• Ratio Cut – Sum along boundary divided by the number

• Normalized Cut – Sum of cut and affinity in foreground and background

• Unbalanced N-cut – N-cut divided by foreground affinity

• Thresholded boundary fraction of a cut

Region Properties

• Area• Perimeter• Relative Centroid• Bounding Box

properties• Fitting Ellipse

properties• Eccentricity• Orientation

• Convex Area• Euler Number• Diameter of Circle

with the same area of the segment

• Percentage of bounding box covered

• Absolute distance to the center of the image

Gestalt Properties

• Inter-region texton similarity

• Intra-region texton similarity

• Inter-region brightness similarity

• Intra-region brightness similarity

• Inter-region contour energy

• Intra-region contour energy

• Curvilinear continuity

• Convexity – Ratio of foreground area to convex hull area

Feature Importance for the Random Forest regressor

Feature Importance

How to Model Segment Quality ?

Best overlap with a ground truth object computed by intersection-over-union.

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Credit: J. Carreira

Diversifying the Ranking

Diversified

Original

Best two hypotheses

Middle two hypotheses

Worst two hypotheses

Segment Ranking using Maximum Marginal Relevance

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Performance

Credit:SasiKanth BendapudiYogeshwar Nagaraj

Ranking

79Credit: J. Carreira

Running Demos

• Methodologies employed– Kmeans using:

• Texture• RGB• Texture + RGB• RGB + HSV• Texture + Lab + HSV

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Running Demos

• Data set used– Microsoft Research Cambridge Object Recognition

Image Database, version 1.0.– Used: 7 classes with 23 per class

• Animal-grass

• Trees-sky-grass

• Buildings-sky-grass

• Airplanes-sky-grass

• Animal-grass• Faces-BG

• Car-wall-ground81

Experiment ResultsFeatures Texture Texture +

RGBRGB RGB +HSV Texture+Lab+

HSV

Animal-grass 72.7% 74.1% 72.3% 72.6% 74.1%

Trees-sky-grass

37.1% 37.1% 40.7% 38.2% 37.1%

Buildings-sky-grass

44.6% 42.8% 51.9% 45.4% 44.7%

Airplanes-sky-grass

58.8% 58.8% 54.6% 59.7% 58.7%

Animal-grass 64.8% 64.8% 69.3% 71% 64.9%

Faces-BG 100% 100% 100% 100% 100%

Car-wall-ground 67.2% 67.2% 68.4% 64.9% 67.2%

Mean 63.6% 63.5% 65.3% 64.6% 63.8% 82

Experiment ResultsFeatures Textur

eTextur

e + RGB

RGB RGB +HSV Texture+Lab+ HSV

One iteration Elapsed time is

7.42 secs

12.26 secs.

1.62 secs

1.5 secs 7.84 sec

Overall Elabsed time for experiment

19.9 mins

32.9 mins

4.4 mins 4 min 21 mins

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Microsoft Research Cambridge Object Recognition Image Database, version 1.0.

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Acknowledgment

• Dr. Devi Parikh

• Dr. Joao Carreira

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Documents

Object Segmentation