Feature
Descriptor
Our
descriptor tried to extend the SIFT feature descriptor, using both an image's
gradient information and an image's gray histogram information. Different from the
original SIFT descriptor, we extended the features of a point from 16*8 to
16*16, adding 16*8 features for image around each feature point to represent
the eight major gray scale component of the image. The log-polar binning
structure is used to divide a round image patch around a feature point into 16
sub image patches. It also used the way MOPS use to find a normalized direction
for wrapping the image patch, so that the algorithm is rotation invariant.
The
values of the eight new features are calculated in this way. For each sub image
patch (one of the 16 component of the round area surrounding a feature point),
we find the eight discontinuous highest peaks in gray histogram of the image
patch. Each peak represents a major gray scale component of the image patch,
and we will then calculate the amount of pixels around the peak (within gray
scale variation of around 10) in the histogram graph. The eight numbers (the
number of pixels of the 4 peaks) are used as 8 features of the image patch. We
then use the total number of pixels in the image patch to normalize the feature
values.
Design
Choice
The
algorithm is designed because histogram information is as important as gradient
information for an image. By adding the new feature component, we can have a
combination of edge information of an image patch, and gray scale distribution
of the image patch. At the same time, the method will be invariant to rotation,
scaling, and all other transformation that the original SIFT has.
Performance
1.
ROC curves
(1)
ROC curves for the Graf
images
(2)
The ROC curves for the
Yosemite images
2.
Image of Harris operator
(1)
Feature Detection for the
Graf images
(2)
Feature Detection for
the Yosemite images
3.
AUC for the four classes
Bike
SSD: average
AUC: 0.546432
RD: average
AUC: 0.587175
Graf
SSD: average
AUC: 0.537375
RD: average
AUC: 0.564156
Leuven
SSD: average
AUC: 0.499849
RD: average
AUC: 0.537125
Wall
SSD: average
AUC: 0.508768
RD: average
AUC: 0.526598
Strength
& Weakness
The
strength of the method is that the descriptor describes information of an image
patch from multiple angles, both its gradient and its histogram. Thus it can be
invariant to a lot of factors, including those that SIFT is invariant to.
Weakness of the method is that, because of the time limitation, the model is
not refined and parameters are not carefully picked. So the test results are
not well now. But there is large space for improvements.
Feature
Detection Results from Two Personal Images