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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: - PowerPoint PPT Presentation
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Semantic Texton Forests for Image Categorization and
Segmentation
We would like to thank Amnon Drory for this deck
הבהרה: מופיע / לא המופיע זה ולא בכיתה הנלמד החומר הוא המחייב החומר
במצגת.
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 34 5 6 7
8 9 1011
17
8485 86
8791
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).