Binary classification of images using Bayes classifier
There are defined 3 usage cases
- Evaluate trained classifier on the test set
./bayes --evaluate --test p1.txt n1.txt --train p2.txt n2.txt --threshold NUM [...]
- Get table which contains precision and recall for possible threshold values (computed using cross-validation)
./bayes --analyze --train pos.txt neg.txt [--q 2^NUM] [--method BAYESIAN_RGB | --method BAYESIAN_R] [--subsample]
- Calculate a probability for image
img.bmp
(only .bmp format supported)
./bayes --predict --train pos.txt neg.txt --image img.bmp [--q 2^NUM] [--method BAYESIAN_RGB | --method BAYESIAN_R] [--subsample]
Run ./bayes VARIANT INPUT OPTIONAL
where
-
VARIANT
-
--evaluate
: evaluation of implemented method -
--analyze
: show table of rates for training samples -
--predict
: predict probability for sample using defined threshold -
INPUT
-
--test positive.txt negative.txt
-
--train positive.txt negative.txt
-
OPTIONAL
-
--method
: possible valuesBAYESIAN_R
orBAYESIAN_RGB
(default isBAYESIAN_RGB
) -
--q NUM
: change size of histogram dimensions (default 16) -
--subsample
: subsample images to descrease exec time (default not use)
./bayes --evaluate --threshold 0.37 --subsample
./bayes --evaluate --train p1.txt n1.txt --test p2.txt n2.txt --threshold 0.34
./bayes --analyze --train p.txt n.txt
./bayes --train p1.txt n1.txt --test --image img.bmp