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
We would like to thank Amnon Drory
for this deck
החומר המחייב הוא החומר הנלמד בכיתה ולא זה המופיע / לא מופיע במצגת.
MSRC 21 Database
For added strength, create several trees instead of just one.
Each tree is trained using a different subset of the training data.
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:
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.
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.
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.
Overall Accuracy: 72%
Though less aesthetic, these results are quantitatively
almost as good as those of TextonBoost.
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