Not the whole range of feature parameters
is necessarily covered by every subsample, or even full sample of features
from an object.
Hence, some bins of a histogram may remain zero.
This leads to numerical problems when computing divisions or logarithms.
In such cases, all zero bins of a histogram are set to a common value, lower
than the lowest non-zero value occurring in all histograms.
This value has the effect of a penalty term.
In this section, denotes the histogram of an object model
from the database.
We define six different criteria that we will evaluate for their classification
performance.
Five of them are based on comparison of
to a histogram
that is
built at recognition time from a test object
.
One implements the maximum-likelihood classifier.