好き? (Activity # 15)

Activity # 15 is pretty much like the previous activity except for a little improvement--this time, it uses probabilisitic classification to minimize the risk or loss in classification.

Since my classes can be linearly separated (i.e. one can separate one from the other by drawing a line in the plot in the previous activity), the method I used was Linear Discriminant Analysis, the details of which can be found here.

This time, we were asked to sort out an image with patterns that are close to being similar, i.e. they should have little to no difference. I generated another image of blobs using Photoshop--the blobs are one brush size apart in terms of size and a difference of 50 ticks when it comes to G-channel color value.

Of course, the script is pretty much plug and play.. all that was needed was to obtain data using the script from the previous activity, dump them into an outfile, get the LDA script (found in the tutorial but implemented in scilab) to organize and process the files, and poof! we have an LDA plot as shown above.

Note that unlike the previous activity, it organized all the data points in a line, and that the discriminant functions were successful in classifying close patterns by increasing the difference between them (note the order of magnitude - xE+06). As expected from the image, the LDA results to a perfect classification (100% correct). I'm not sure about real life images though. I'll probably give it a shot when I find some time.

That's why, it's probably just a 9 for now.