On the Design of a Photo Beauty Measurement Mechanism Based on Image Composition and Machine Learning
學年 106
學期 1
出版（發表）日期 2017-10-25
作品名稱 On the Design of a Photo Beauty Measurement Mechanism Based on Image Composition and Machine Learning
作品名稱（其他語言）
著者 C. S. Fahn; M. L. Wu
單位
出版者 IntechOpen
著錄名稱、卷期、頁數 Perception of Beauty
摘要 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.
關鍵字 computational aesthetics;photo beauty measurement;image composition;machine learning;decision tree;multilayer perceptron
語言 en_US
ISBN
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/120324 )

SDGS 優質教育,尊嚴就業與經濟發展,產業創新與基礎設施