This compares the difference of using a neural network and a support vector machine for classifying images based on color. The exact application is differentiating between pictures taken during the day and pictures taken during the night.
Both scripts assume that the data is all in a root folder, with each subfolder (being a label) containing images. Ex.
images/
--- day/
------ image1.png
------ image2.png
------ ...
--- night/
------ image1.png
------ image2.png
------ ...
A complex feature extraction is not needed for classifying images based on color. This uses the image's RGB matrix flattened and normalized, i.e each entry in the flattened matrix is divided by the mean of all the entries.
The SVM takes a much shorter time to train, and does much better in testing. The SVM gets about 89% accuracy whereas the neural network gets about 78% accuracy, both resizing the images to 30x30.
python neural.py images/
python svm.py images/
neural_model.pkl and svm_model.pkl respectively will be saved to the
same directory and loaded automatically if it is detected.