Semantic texton forests for image categorization and segmentation
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Semantic Texton Forests for Image Categorization and Segmentation. We would like to thank Amnon Drory for this deck. הבהרה: החומר המחייב הוא החומר הנלמד בכיתה ולא זה המופיע / לא מופיע במצגת. . Semantic Texton Forests. Input:

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Semantic Texton Forests for Image Categorization and Segmentation

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Semantic texton forests for image categorization and segmentation

Semantic Texton Forests for Image Categorization and Segmentation

We would like to thank Amnon Drory

for this deck

הבהרה:

החומר המחייב הוא החומר הנלמד בכיתה ולא זה המופיע / לא מופיע במצגת.


Semantic texton forests

Semantic Texton Forests

  • Input:

  • Training: Images with pixel level ground truth classification

MSRC 21 Database


Semantic texton forests1

Semantic Texton Forests

  • Input:

  • Training: Images with pixel level ground truth classification.

  • Testing: Images

  • Output:

    • A classification of each pixel in the test images to an object class.


Main mathematical tools

Main Mathematical Tools

  • Conditional Random Fields (CRF)

  • Textons( Convolution with Filter Banks )

  • K-Means

  • Joint Boost

  • Randomized Decision Forests


Decision trees

Decision Trees


Decision trees1

Decision Trees


Decision rules split functions

Decision Rules (Split Functions)

  • Choose one or two pixels near pixel of interest

  • Calculate simple function of their values:

    • Difference

    • Sum

    • Absolute Difference

    • Or just the pixel valuere To a hreshold

  • Motivation: very small number of computer operations. Easy to implement on GPU.


Training the tree

Training the tree

  • For each node:

  • Randomly generate a few decision rules

  • Choose the one that maximally improves the ability of the tree to separate between classes. (E(I) – entropy)

  • Stop when tree reached pre-defined depth , or when no further improvement in classification can be achieved.


Decision forests

Decision Forests

For added strength, create several trees instead of just one.

Each tree is trained using a different subset of the training data.


Classifying at test time

Classifying at test time

0.001

0.04

0.05

0.23

0.01

For each pixel in the test image:

Apply the segmentation forest – marking a path in each tree (yellow).

Each leaf is associated with a histogram of classes.

Average the histograms from all tree, achieving a vector of probabilities for this pixel belonging to each class:


Classifying at test time1

Classifying at test time

0.001

0.04

0.05

0.23

0.01

The probability vectors derived from the Decision Forests can be used to classify pixels to classes, by assigning to each pixel the label that is most likely. The results are very noisy.


Paths on trees represent texture

Paths on trees represent Texture

1

84

85

86

2

3

4

87

7

6

5

91

9

10

8

11

17

The texture of an area around a pixel can be represented by a vector comprised of all the nodes in the decision forest that belong to paths traversed when applying the forest to this pixel. In the above example, this would be the vector:

( 1, 3, 6, 10, 17, … , 84, 85, 87, 91 )

This vector is called a Semantic Texton.


Example of semantic textons

Example of Semantic Textons

A visualization of leaf nodes from

one tree (distance d = 21 pixels). Each patch is the average of all patches in the training images assigned to a particular leaf node. Features evident include color, horizontal, vertical and diagonal edges, blobs, ridges and corners.


Example of semantic textons1

Example of Semantic Textons


Second randomized decision forest

Second Randomized Decision Forest

  • The split functions at the nodes are based on the Texture-Layout filter:

  • Two types of Texture-Layout filters are used:

  • Count the occurrence of a certain node in the semantic textonsof pixels in a rectangle.

,

(

)


Second randomized decision forest1

Second Randomized Decision Forest

  • The split functions at the nodes are based on the Texture-Layout filter:

  • Two types of Texture-Layout filters are used:

  • Count the occurrence of a certain node N in the semantic textons of pixels in a rectangle.

  • Sum the probabilities of belonging to a certain class K for all pixels in a rectangle. This is semantic context.

,

(

)


Stf results

STF - Results

Overall Accuracy: 72%

Though less aesthetic, these results are quantitatively

almost as good as those of TextonBoost.


Stf summary

STF - Summary

  • To speed up calculation, this algorithm uses Radomized Decision Forests instead of other mathemaical tools used in TextonBoost.

  • One RDF is used to calculate texture.

  • A second RDF is used to classifiy pixels to object classes.

  • The results are almost as good (quantitatively) as those of TextonBoost.

  • The algorithm is much quicker than TextonBoost.


Real time human pose recognition in parts from single depth images

Real-Time Human Pose Recognition in Parts from Single Depth Images

J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R. Moore, A. Kipman, A. Blake


Overview

Overview


Classification

Classification

  • Classify each pixel to one of 31 body part classes:

    • Left Upper / Right Upper / Left Lower / Right Lower head

    • neck

    • Left / Right shoulder

    • Left Upper / Right Upper / Left Lower / Right Lower arm

    • Left /Right elbow

    • Left /Right wrist

    • Left /Right hand

    • Left Upper / Right Upper / Left Lower / Right Lower torso

    • Left Upper / Right Upper / Left Lower / Right Lower leg

    • Left /Right knee

    • Left /Right ankle

    • Left /Right foot


Randomized decision forests

Randomized Decision Forests

  • The Randomized Decision Forests are very deep (depth = 20).

  • => A very strong ability to classify correctly on the Training Set.

  • => A risk of over fitting. This risk is averted by using a very (very) large training set, containing examples of many poses we wish to recognize. Most of this training set is artificially generated.


Decision rules

Decision Rules


Classification body part locations

Classification → body part locations

  • Separately for each of 31 Body Part Classes:

    • Create Probability map

    • Project to 3D space

    • Find modes ( using mean-shift )

    • The modes (with a little post processing ) are the suggested body part locations (the output of this algorithm).


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