Combining shape and color features (at long last)

Our current system implementation simply outputs a class label for a given example image. We instead propose an output of "likelihoods" for every class.

For example, instead of "someimage.jpg" = Stenodynerus chinesis,

we might say "someimage.jpg" is 90% likely to be Stenodynerus chinesis, 5% likely to be ..., etc...

Creating these "confidence" vectors allows us to combine shape and color features assuming that they are independent.

P( class | shape AND color ) = P( class | shape ) * P( class | color )

For this, we switch our implementation to one-vs-all SVMs due to limited time. We only briefly looked into probabilistic output, and this choice we made due to simplicity of one-vs-all approach.

Results of one-vs-all SVMs using color features with standard deviation:

92.906 ± 4.534 % (406/437)

Results of one-vs-all SVMs using HOG features w/ stddev:

 86.499 ± 4.843 % (378/437)

Finally, one-vs-all SVMs using combined color and HOG:

96.110 ± 1.810 % (420/437)

When we combine the features, accuracy improves and stddev decreases. This just about wraps up my project for the summer. This blog was used mainly for documentation purposes. You can find the full research paper here, or the PowerPoint presentation here.

Thanks for reading!