Project 2: Object detection by Antiface projections
Description
Implement
the Antiface
method for detection.
Implement
the following extensions of Antifaces:
In
the original method an image is classified as object if it is accepted by all
antiface detectors, and the detection process is sequential. Change the
detection process so that each antiface detector is applied to all sub-images.
Implement the new acceptance rule: a sub-image is classified as object if it is
accepted by 80% of antiface detectors.
Project
the training images of the class on 10D antiface space. Find the parameters of
Gaussian distribution that best describe the projections (using Maximum
Likelihood Estimation). Since detectors are independent of each other, the
covariance matrix can be assumed diagonal. Perform recognition using Naïve Bayes.
Project
each image onto 20D antiface space, spanned by antiface detectors and train SVM
in this space. Recognition should be done in similar way (projection onto
antiface space and SVM classification).
Results
The detection results should
be presented in the form of ROC curve, which shows the performance of the
detection method by changing the threshold that controls the detection score.
The x-axis is the number of true positives (faces found by the system) divided
by the total number of faces in images (from the ground truth). The y- axis is
(1- number of false positive, divided by the total number of non-face
sub-images). False positive is a sub-image that the system detected as a face but
its not marked as a face in the ground truth file.
Data sets
The system should be tested
on CBCL face set.
This data set contains images for training and for testing.
SVM Package
Useful Links
Class lecture on object
detection
Slides explaining the
antiface method
Matlab statistics toolbox. I suggest implementing this project in Matlab
using its statistics toolbox. It contains functions for MLE, Likelihood, and
other useful stuff.
Status
Claimed by Shira and Ariel Gorfinkel