We are making a classification model that takes a clip of a movie as an input and predicts who the director is. Roughly speaking, it will be a model that understands film directors' unique style.
We are going to extract 5 features from a movie: a visual-feature vector from ResNet pretrained on ImageNet, an audio-feature vector from an audio model, shot type of a frame (one-hot encoding), emotion detection, and a color scheme of a frame. In the end, we are going to concatenate all these feature vectors and give it as a input for our classification model.
We find a tool that can extract color scheme(or palette) of an image as below. It both has information about colors and their proportion. However, I can't think a smart way to convert this information into 1-d vector form. Any ideas?
Of course I know the ResNet will get information about colors but the importance of color will be degraded in ResNet. I think the color is very important feature in defining a director's style and thus I want to use a color feature separately.