Final Experiment Using Colour Histogram Features

Posted: September 27, 2019 at 11:29 am

My talos search using a 24 bin colour histogram finished. The best model achieved accuracies of 76.6% (training), 74.6% (validation) and 74.2% (test). Compare this to accuracies of 93.3% (training), 71.2% (validation) and 72.0% (test) for the previous best model using initial features. On the test set, this is an improvement of only ~2%. The confusion matrix is quite a lot more skewed with 224 false positives and only 78 false negatives. Compare this to 191 false positives and 136 false negatives for the previous best model using initial features. As the histogram features would need to be calculated after rendering, I think it’s best to stick with the initial features where the output of a generator can be classified before rendering, which will be much more efficient.

The following images show the new 100 compositions classified by the best model using these histogram features.

“Good” Compositions
“Bad” Compositions

While writing this post, I trained a “final model” trained on the histogram features. That skewed confusion matrix may actually work better; the “good” compositions are certainly tighter with very few clearly weak results. At the same time there are only a few good results in the bad class. I need to spend a little more time to consider these results and decide whether the histogram features are enough of an improvement to balance the cons.

“Good” Compositions
“Bad” Compositions