**On the Design of a Photo Beauty Measurement Mechanism Based on Image Composition and Machine Learning** On the Design of a Photo Beauty Measurement Mechanism Based on Image Composition

DOI: 10.5772/intechopen.69502

Chin-Shyurng Fahn and Meng-Luen Wu

Additional information is available at the end of the chapter Chin-Shyurng Fahn and Meng-Luen Wu

http://dx.doi.org/10.5772/intechopen.69502 Additional information is available at the end of the chapter

and Machine Learning

#### Abstract

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In this chapter, we propose a machine learning scheme on how to measure the beauty of a photo. Different from traditional measurements that focus on the quality of captured signals, the beauty of photos is based on high-level concepts from the knowledge of photo aesthetics. Because the concept of beauty is mostly defined by human being, the measurement must contain some knowledge obtained from them. Therefore, our measurement can be realized by a machine learning mechanism, which is trained by collected data from the human. There are several computational aesthetic manners used for building a photo beauty measurement system, including low-level feature extraction, image composition analysis, photo semantics parsing, and classification rule generation. Because the meaning of beauty may vary from different people, the personal preference is also taken into consideration. In this chapter, the performance of two computational aesthetic manners for the perception of beauty is evaluated, which are based on image composition analysis and low-level features to determine whether a photo meets the criterion of a professional photographing via different classifiers. The experimental results manifest that both decision tree and multilayer perceptron-based classifiers attain high accuracy of more than 90% for evaluation.

Keywords: computational aesthetics, photo beauty measurement, image composition, machine learning, decision tree, multilayer perceptron
