new tool phi transform
I'm still stumbling in the dark on this topic! Today I implemented phi-transform. The idea is break an image into k*k image-ngrams, then find the most similar layer-1 superposition (see yesterday's work), and substitute that in, in its place. Then map back to an image, and then average them all. The code is here, but you will need to understand some of the project for it to make sense. Anyway, the goal is to do a little normalization. The more normalization we can do, the easier pattern recognition becomes.
First, our method:
-- create 100 mnist test images (using the test-csv):
$ ./mnist-data-to-images.py 100
-- filter the sw-file from yesterday down to the layer-1 results (that is all we need):
$ cd sw-examples/
$ grep "^layer-1 " mnist-100--save-average-categorize--0_5.sw > mnist-100--layer-1--0_5.sw
-- phi-transform the test images (with a run-time of 62 minutes):
$ cd ..
$ ./phi-transform.py 10 work-on-handwritten-digits/test-images/*.bmp
-- make pretty pictures:
$ cd work-on-handwritten-digits/
$ ls -1tr test-images/*.bmp > image-list.txt
$ montage -geometry +2+2 @image-list.txt mnist-test-100.jpg
$ ls -1tr phi-images/* > image-list.txt
$ montage -geometry +2+2 @image-list.txt mnist-test-100--phi-transform-mnist-100-0_5.jpg
And now the results. First the 100 test images:
Now, after the phi-transform:
So, it is sort of normalized a bit. But I'm not sure it is a useful improvement. This phi-transform only used the average-categorize-image-ngrams from 100 training images. What if we ramp that up to 1000? And of course, there are two other variables we don't know the best values for. The image-ngram size, and the average-categorize threshold. Currently we are using 10, and 0.5 respectively. Also, I don't know if the phi-transform is better or worse than just doing a simple Gaussian blur. See my edge-enhance code for that. And of course, there is always the possibility that average-categorize won't help us at all, but I suspect it will.
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by Garry Morrison
email: garry -at- semantic-db.org