Verifying the Feature Vectors

Experiment 1: To verify if the attributes would classify any easy pattern examples the following experiment was conducted.

1. Two Brodatz textures where chosen.
1. http://www.ux.uis.no/~tranden/brodatz/D15.gif
2. http://www.ux.uis.no/~tranden/brodatz/D32.gif
2. Random points were chosen inside the image. The following code was used to generate the points inside the image. /usr/sci/crcnsdata/CRCNS/Synapses/Code/Matlab/20080721/getPointsFromTexture.m
   
3. The features explained in this blog where generated using the code following code. /usr/sci/crcnsdata/CRCNS/Synapses/Code/Matlab/20080721/momentCalc.m. The features are the same. The only difference is that in that experiment the features were generated for SIFT key points where as here it is for random points.
   
4. Then the classifier was run over the dataset. The results are as follows.

Experiment 2: The same experiment was run for a different set of images. The features where finding it difficult to segregate these data points into appropriate classes. The images used for this experiment are

1. http://www.ux.uis.no/~tranden/brodatz/D3.gif
2. http://www.ux.uis.no/~tranden/brodatz/D24.gif

The classifier would not classify the images correctly. The distribution of the attributes used in the stumps in few of the nodes are noted below. The histograms of different attributes are shown as below:

1. The blue gives the distribution of the particular attribute for the given data points at that node.
2. The green histogram is the distribution of the positive synapse regions alone.
3. The red histogram is the distribution of the negative synapse regions alone.

The below graphs are the different metrics of the classifier that is learned, These are results of one of the successful runs where all 8 nodes of the cascade where constructed. But in most other runs the classifier failed.
The next step is to modify the experiment to fit in the linear classifier instead of the Decision stump classifier.