Targeted Wavelet Based Image Aesthetics Classification Using Convolutional Neural Nets

Image aesthetics classification is the method of visualizing and classifying images based on the visual signatures in the data rather than the semantics associated with it. In this work, we develop learning techniques that is inspired by the way a human brain identifies images. We develop CNN models by providing most useful information to the network by leveraging the joint information from wavelet compressed image patches and class activation maps (CAM). The performance of the network in recognizing the image based on simple visual aesthetics signatures is shown to be better than existing techniques with few caveats.