Computational aesthetics of photos quality assessment based on improved artificial neural network combined with an autoencoder technique

Photograph aesthetical evaluation has been widely investigated in these decades. For fine-granularity aesthetic quality prediction, a novel aesthetics classifier based on improved artificial neural network combined with an Autoencoder technique is presented. First, we download large consumer photographic images from a well-known online photograph portal. Then, we extract 56 features normalized to 0–1 and train the networks with photographs of high and low ratings to test the quality of photos. Experimental results show that the accuracy of classification is above 86.67%, which is better than all state-of-the-art methods. Meanwhile, it is observed from experiments that the extracted features are consistent with the humans׳ visual perception systems.