1. Introduction

The computational aesthetics is a field of research to measure the beauty of photos. There are many benefits for predicting the aesthetics score of a photo, for example, computers can aid to manage a huge amount of photos according to the perception of beauty, and can assist to predict whether a photo will be favorable when it is made public in advertisements. The perception of

© 2017 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited.

the beauty of a photo does not concentrate on the measurement of signal quality only, but also cares about the meaning of the concept of beauty defined by human being. In other words, a sharp and clear photo is not always more beautiful than those are not, but it depends on the contents in the photo that delights people. Usually, photos taken by professional photographers are better than amateurs. Many works focus on determining whether an input photo is taken by professionals [1]. Figure 1 illustrates an example of two photos taken by a professional and an amateur, respectively. The photo in Figure 1(a) is taken by a professional photographer, which has better contrast, color harmony, and sharpness. Besides, the contents of the photo are rather simple. In Figure 1(b), the photo taken by an amateur has less color harmony apparently, which also possesses motion blur. Because computer algorithms can measure the contrast, color harmony, and sharpness, determining whether a photo is taken by a professional is possible.

However, the meaning of beauty is different from people. Essentially, people in different locations or of different ages have different tastes. The tastes of eastern and western people differ obviously, and the tastes of old and young are not the same usually. It reveals that subjectivity does exist in the perception of beauty. In order to deal with the subjectivity, the photo beauty measurement system should be flexible, which can be updated and constructed for different cases. In brief, the measurement system is a classification model that can be trained with respect to different kinds of scenarios.

Currently, with the evolution of computer vision, the topic of computational aesthetics arises. By analyzing the attributes and extracting the features of a photo, computers are able to classify whether a photo is preferred by professional photographers or not. There are many kinds of attributes that can be retrieved from a photo, such as brightness, color contrast, saturation, existence of human faces, animals, sky, and so on. After collecting a huge amount of photos in accordance with their attributes and features, a classification model called classifier can be built through training. The trained classifier can be used to predict the aesthetics score of a photo to distinguish good and bad photos. However, depending on training methods, some of the classifiers are like a black box, where their decision process is not understandable. For example, a multilayer perception (MLP) works mostly on the weights of its synapses connecting with neurons, whose decision process is not readable by humans. The decision tree model is a better choice in this aspect, because every path in the decision tree is a

Figure 1. Two flower photos for beauty measurement: (a) a photo with high aesthetics score; (b) a photo with low aesthetics score.

readable classification rule. Nevertheless, the accuracy of the decision tree model is sometimes worse than the neural networks or other algorithms that do not produce readable classification rules.

In this chapter, we propose a machine learning scheme to measure the beauty of photos. Two computational aesthetics manners for the perception of beauty are tested; the first is with the aid of low-level features, and the second is resorted to image composition analysis. For such photo beauty measurements, we use two machine learning approaches, which are based on neural networks and decision trees. The neural network model has higher accuracy, while the decision tree produces readable classification rules for humans, which is possible for us to perform photo enhancement according to the rules.

The remainder of this chapter is organized as follows. In Section 2, we list some related works of the perception of beauty for digital photos. In Section 3, we elaborate photo aesthetics with regard to low-level feature extraction. In Section 4, we explain photo semantics that possibly influences the beauty measurement. Section 5 describes image composition analysis used for perceiving the beauty of photos. In Section 6, two machine learning approaches of neural networks and decision trees are presented. In Section 7, we take account of personal preference to solve the subjectivity problem by adjusting the bias of input feature values. Section 8 evaluates two computational aesthetics manners for the perception of beauty according to low-level features and image compositions, respectively. Finally, some conclusions are made in Section 9.
