Boosting in Matlab

Boris gave me his Matlab boosting code. His code is an implementation of Adaboost using haar-like features. The weak classifier is just a stump (looks at 1 feature and thresholds it).

The idea behind boosting is that many weak classifiers together create 1 strong classifier. The weak classifiers used in this implementation are extremely trivial and their individual accuracy is not that much better than 0.5. However, as we add more weak classifiers to the overall classifier, the overall accuracy improves, as shown in the following graph:


What is a haar feature?
In this implementation, a random number of rectangles are created, each with a different weight. Note that all of the training images must be the same size. We choose these haar features without seeing the images yet. The sum of the pixels in each of the different rectangles and the weights are used to come up with the feature. Each haar feature will result in 1 singleton value for each image.

Example of haar features:


What you'll need for an 'X' classifier:
1. A pool of images of 'X' (positive examples)
2. A pool of images that do not contain 'X' in them (negative examples)

Set aside part of each of the above pools for testing. The remaining, you'll use for training.

How it will go down:
1. Choose the number of haar features to create, and call this nh.
2. Apply all nh haar features to all of the training images, both positive and negative. As a result, for each image, we will have a feature vector of length nh.
3. Choose the number of weak classifiers desired, and call this nwc. Note that nwc <= nh.
4. Choose nwc of the nh haar features. Ideally, these nwc haar features are the best haar features out of the bunch. This means that these features are the strongest. Associate a threshold for each of the nwc haar features.
5. For each of the test images, find the nwc features. We now have a feature vector of length nwc for each of the test images.
6. Given the threshold for each of the nwc features, come up with a confidence for each test image.

I created an 'a' classifier for my project. I resized all of my training images to 24 by 24 pixels. I used a portion of the images of 'a' as the positive training examples, and used the remainder of them for testing. I used a combination of images of letters 'b' through 'z' as the negative training examples, and a separate portion of those for testing. Using a threshold of 0.5, the number of false positives was 194/1350, which comes out to a rate of 14%. The true positive rate is 100%. Here is the ROC curve:


How this will apply to my project: Given an image of a letter, I will apply all 26 classifiers to it. I will then have a score for each letter, and can use this instead of the nearest neighbor mechanism I was using before.