2. Related works

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

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

trained with respect to different kinds of scenarios.

aesthetics score.

98 Perception of Beauty

There are some existing systems and methodologies for assessing the beauty of photos. Yeh et al. proposed a personalized photo ranking system to assess the beauty of photos manually with some defined criterions [2]. In the system, users have two options to carry out personalization; one is feature-based, and the other is example-based. The photo ranking system is illustrated in Figure 2. In the feature-based option, the system provides a series of feature weights to be adjusted, and the photos will be sorted in the light of their weighted feature

Figure 2. Two kinds of personalized photo ranking options: (a) feature-based; (b) example-based.

scores as Figure 2(a) shows. In the example-based option, the user selects a number of interested photos, and the system extracts the features of these selected photos to produce weights for features automatically as shown in Figure 2(b). The authors also proposed some new features for photo ranking.

In 2012, an intelligent photographing interface with on-device aesthetic quality assessment system is proposed [3], which makes use of five aesthetics perspectives of photography, such as saturation, color, composition, contrast, and richness. The aesthetic quality assessment system works on a tablet with a camera and runs in real time. Figure 3 graphically shows the system, where Figure 3(a) is the overall rating of features in a photo, while Figure 3(b) is a working screenshot of the system.

In consideration of the subjectivity, a digital photo challenge (DPC) platform is established. The platform allows experts to rate a photo at one of 10 aesthetic quality levels, from good to bad. Figure 4 illustrates some photos in the database for example. In Figure 4(a), the left photo is focused on the flowers successfully, and its theme is harmonic, which makes a comfortable feeling. The color of the right photo is also harmonic and looks comfortable. However, in Figure 4(b), two photos are out of focus, with messy colors and motion blur. Most people would agree that photos in Figure 4(a) are better than those of Figure 4(b).

Figure 3. An instant aesthetics quality assessment system: (a) five aesthetics perspectives of photography; (b) an assessment example of the system.

Figure 4. Photos ranked by professionals in a DPC platform: (a) high-score photos; (b) low-score photos.

On the Design of a Photo Beauty Measurement Mechanism Based on Image Composition and Machine Learning http://dx.doi.org/10.5772/intechopen.69502 101

Figure 5. Photo enhancement by the instruction of a decision tree: (a) before processing; (b) after processing.

In Damon Guy's article [4], he defined photographic aesthetics objectively. He analyzed what elements are used in assessing the beauty of photos, which is governed by the "Principle of photographic art." He found there are 15 important elements including unity, harmony, color, variety, movement, contrast, balance, proportion, pattern, rhythm, geometry, focus, viewpoint, blur, and sharpness. Of these features, the geometry element comprises a photo's shape and composition. The viewpoint element examines whether a person in a photo looks at the camera or not. Some elements are easy to understand and implement; therefore, we will emphasize some photograph features, such as color component, sharpness, brightness, contrast, saturation, color balance, colorfulness, and simplicity in this chapter.

The development of computational aesthetics is also helpful for photo enhancement. In 2016, a photo enhancement method based on computational aesthetics was proposed [5]. In their proposal, a decision tree was produced by virtue of machine learning techniques, and a photo was adjusted to meet the conditions of a favorable contemporary style photo according to the tree, if it is classified as not acceptable. An example of photo enhancement is shown in Figure 5, where Figure 5(a) is an original photo, and Figure 5(b) is an adjusted photo by the proposed method that uses only one instruction to improve the input photo. The example in Figure 5 just reduced its brightness via their trained decision tree.
