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Binary tests between a pair of pixels are performed for the classification.
Each patch is guided by the binary tests stored at the nodes.
For efficiency reason, the number of binary tests is determined depend on the depth of the tree.
Binary tests classify items into two categories such as reject/accept or positive/negative.
The authors proposed the decomposition of a M-ary subtest into a set of binary tests, represented in a decision tree, to make the classification process simpler.
During training, for each non-leaf node starting from the root, we generate a large pool of binary tests {ϕ k } by randomly choosing f, r, s, τ, type.
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During this evaluation we used binary test image (200 × 200).
The binary pattern run length matrix is proposed for binary test.
That is, the number of the binary test increases with increasing the depth of the tree.
The key parameters of the binary test of each node are optimized using information gain.
The resulting optimum binary test improves the discriminative power of individual trees in the forest.
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