Photo Composition Analysis Based on Deep Neural Networks

  • 吳 敏慈

Student thesis: Master's Thesis


In order to take better photos it is a fundamental step for the beginners of photography to learn basic photo composition rules However there are no tools developed to help beginners analyze the composition rules in given photos Thus in this study we developed a method with the capability to identify 12 common composition rules in a photo It should be noted that some of the 12 common composition rules have not been considered by the previous studies and this deficit gives this study its significance and appropriateness In particular we utilized deep neural networks (DNN) to extract high-level semantic features for facilitating the further analysis of photo composition rules In order to train the DNN model we constructed a dataset which is collected from some famous photo websites such as DPChallenge Flicker and Unsplash All the collected photos were later labelled with 12 composition rules by a wide range of raters recruited from Amazon Mechanical Turk (AMT) Three DNN architectures (AlexNet GoogLeNet and ResNet) were then employed to predict the composition of the collected dataset The representative features of each composition rule were further visualized in our system The results showed the feasibility of the proposed method and revealed the possibility of using this method to assist potential users to improve their photographical skills and expertise
Date of Award2018 Aug 31
Original languageEnglish
SupervisorMin-Chun Hu (Supervisor)

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