This block learns an SVM using an input kernel and uses it to classify segment histograms.
BK = BLOCK_CLASSIFY_SVM() Initializes the block with the default options.
BK = BLOCK_CLASSIFY_SVM(BK) Executes the block with options and inputs BK.
Required Inputs:
- kernel
A pre-computed kernel block
- hist
Segment histograms
Options:
- bk.seg_neighbors
The number of neighbors to include in the histogram. Default 0.
- bk.svm_type
The type of SVM to learn with libSVM. Default 'C'.
- bk.svm_C
The value of C to use. Default 1.
- bk.svm_nu
The value of nu to use. Default 0.5.
- bk.svm_balance
Balance the svm? Default 0.
- bk.svm_cross
Perform N-fold cross validation. Default 10.
- bk.svm_rbf
Use an rbf kernel? Default 1.
- bk.svm_gamma
Gamma for the rbf kernel. Default [] means automatically determine a good gamma.
- bk.debug
Run the SVM in debug mode? Default 0.
- bk.verb
Be verbose? Default 1.
- bk.probability
Compute probability output? Default 0.
- bk.bg_cat
Category to assign the segment to if it has an empty histogram. Default 0.
Fetchable attributes:
- type
The type of classifier used 'svm'
- cl
A structure representing the classifier.
Block Functions:
- function [class confidence] = classify(cl, h)
Classify histogram h with cl using the SVM.