The new gentle boost classifier

After the kNN classifier experiment, we will be trying a new classifier inspired by one of the poster in MIABB 2008 - Mitochondria detection in electron microscopy images. The salient features of this classification mechanism are the following:

1. Pre-processing: The image is histogram equalized and median filtered. Histogram equalization will help reduce intensity variations across the image and median filtering will decrease the salt & pepper kind of noise in the image.
2. Features: Histograms and Gabor filter responses for multiple window sizes and multiple Gabor filter frequencies are used as features for the classifer.
3. Ground truth markup: The entire Synapse region is marked up for hte experiment and all the marked up pixels will be used as positive examples for the training the classifer.
4. Learning Algorithm: The learning algorithm uses Gentle Boost (a variation of AdaBoost) to train the classifer. The classifer is not used specified in the poster.