semantic texton forests for image categorization and segmentation we would like to thank amnon drory...

Semantic Texton Forests for Image Categorization and

Segmentation

We would like to thank Amnon Drory for this deck

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Semantic Texton Forests Input: 1. Training: Images with pixel level ground truth

classification

MSRC 21 Database

Semantic Texton Forests Input: 1. Training: Images

with pixel level ground truth classification.

2. Testing: Images

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

Main Mathematical Tools

1. Conditional Random Fields (CRF)

2. Textons ( Convolution with Filter Banks )

3. K-Means

4. Joint Boost

Randomized Decision Forests

Decision Trees

Decision Trees

Decision Rules (Split Functions)

1. Choose one or two pixels near pixel of interest

2. Calculate simple function of their values:- Difference- Sum- Absolute Difference- Or just the pixel

valuere To a hresholdMotivation: very small number of computer operations. Easy to implement on GPU.

Training the treeFor each node:1.Randomly generate a few decision rules2.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

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

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: 0.0

10.23

0.05

0.04

0.001

Classifying at test time

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.

0.01

0.23

0.05

0.04

0.001

Paths on trees represent Texture1

2 3

4 5 6 7

8 9 1011

17

84

85 86

87

91

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

A visualization of leaf nodes fromone 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 Textons

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:1. Count the occurrence of a certain node in the

semantic textons of pixels in a rectangle.

( , )

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:1. Count the occurrence of a certain node N in the

semantic textons of pixels in a rectangle.2. Sum the probabilities of belonging to a certain class K

for all pixels in a rectangle. This is semantic context.

( , )

STF - Results

Overall Accuracy: 72%Though less aesthetic, these results are quantitatively

almost as good as those of TextonBoost.

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

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

A. Kipman, A. Blake

Overview

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

• 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

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).