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

摘要

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. (C) 2015 Elsevier B.V. All rights reserved.