Title: Rejection Based Classifier
for Target Detection in Images
By: Michael Elad*
Affiliation: SCCM program -
Abstract:
One of the most fundamental problems in the treatment of high-dimensional data
is classification of a cloud of points in R^D into several sub-classes based on
training data. An important such task is the pattern detection problem in
images, which requires a separation between 'Target' and 'Clutter' classes,
where every instance of a pattern in each of these classes appears as
a sequence of D pixels. In most cases, the probability of the 'Target'
class is substantially smaller compared to that of the 'Clutter'. In this talk
we describe a new classifier that exploits this property, yielding a low
complexity yet effective target detection algorithm. This algorithm, called the
Maximal Rejection Classifier (MRC), is based on successive rejection
operations. Each such rejection stage is performed using a linear projection
followed by thresholding. The projection direction is
designed to maximize the number of rejected 'Clutter' points from further
consideration. An application of detecting frontal and vertical faces in images
is demonstrated using the MRC with encouraging results.
* Joint work with Yacov Hel-Or,
IDC,