In recent years, there have been attempts to discover the principles that determine the value of aesthetics in the domain of computing. Many and diverse studies have tried in some way to capture these principles through technical characteristics. To this end, helped by the ease of Internet data acquisition, datasets of images have been published which were obtained online at random from websites and photography competitions. To guarantee the validity of a system of aesthetic image classification, one must first guarantee its capacity for generalization. This paper studies how the indiscriminate selection of images can affect the generalization capacity obtained by a binary classifier.

This paper presents the results of two experiments comparing the functioning of a computational system and a group of humans when performing tasks related to art and aesthetics. The first experiment consists of the identification of a painting, while the second one uses the Maitland Graves's aesthetic appreciation test. The proposed system employs a series of metrics based on complexity estimators and low level features. These metrics feed a learning system using neural networks. The computational approach achieves similar results to those achieved by humans, thus suggesting that the system captures some of the artistic style and aesthetics features which are relevant to the experiments performed.

The present paper describes a series of features related to complexity which may allow to estimate the complexity of an image as a whole, of all the elements integrating it and of those which are its focus of attention. Using a neural network to create a classifier based on those features an accuracy over 85% in an aesthetic composition binary classification task is achieved. The obtained network seems to be useful for the purpose of assessing the Aesthetic Composition of landscapes. It could be used as part of a media device for facilitating the creation of images or videos with a more professional aesthetic composition.