為了拍出更佳的照片，攝影的初學者通常會學習一些基礎的構圖規則。然而，目前就我們所知，並無攝影學習工具是針對構圖所設計的。因此，在這篇論文當中，我們發展了一個可以辨識出照片之中，是否有我們所選擇的12種基礎構圖的方法。值得注意的是，這12種構圖，有些是以往的研究之中被未被探討過的。我們的方法採用的是以深度類神經網路，萃取出照片之中的高層次語意特徵，藉此來協助判斷照片之中的構圖。為了有效的訓練我們的網路，我們建立了一個構圖資料庫，裡面的照片分別是從三個知名攝影網站(DPChallenge, Flicker, and Unsplash)所搜集而來，這些照片又經過Amazon Mechanical Turk (AMT)這個群眾外包平台來標記出照片裡所含有的構圖。實驗結果也顯示了我們方法的可行性，以及展示了未來對幫助潛在攝影初學者學習攝影構圖的可能。

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.

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