近年來，由於數位相機與手機內建的相機的盛行，拍照變得越來越簡單，而照片的數量也大量的增加。照片可以用來表達拍照者的情感與對美學的敏感度，其中攝影構圖為拍照時的一個重要元素。攝影構圖的目的為將照片中的主題妥善安排，讓畫面的整體呈現平衡感。儘管攝影構圖在專業的、優秀的攝影作品中為一個重要因素，目前有關攝影構圖的研究並未有大量的研究。
在本篇論文，我們提出了一套應用決策樹演算法的系統來辨認所欲處理照片的構圖類別。在辨認構圖類別前，我們使用了不同的方法將照片中的相關構圖類別的特徵擷取出來，例如霍夫轉換中線的偵測、找顯著區等等。138個特徵被截取出來後，當成輸入的參數來進行下一步的分類。但在使用決策樹演算法分類前，必須進行前處理步驟，我們採取最小熵離散化方式將連續的數值轉換為離散數值。進行離散化後，使用C4.5決策樹演算法辨識出照片的攝影構圖類型。
實驗的部份我們針對不同的攝影構圖類型進行分析。我們提出的方法可以有效地對照片的攝影構圖類型進行分類，正確率為88.2%。

In recent years, owing to the popularity of digital still camera and camera module of the mobile phone, taking photos becomes easier than before and the number of photos grows exponentially. Photography is a kind of forms of art that the photographers can convey their emotion and aesthetic sensibilities. The photographic composition is one of the important components in the photo. The aim of the composition is to make the photo look balance by arranging subjects in the photo properly. Although photo composition is one of the most important attributes when evaluating image appeal, current computational approaches do not analyze features related to image composition in depth.
In this paper, we present a simple and effective decision tree learning method for classifying the photo composition. The classification method adopts different approaches to extract photo features, such as Hough transform for line detection, calculating nearest distance between centroid of the saliency map and the center point (or four power points) of the image, feature matching for symmetry detection, and sobel edge operator for local linearity and sharpness of the photo. 138 features are employed as input variables.
Before applying C4.5 decision tree algorithm, because these input variables are continuous numeric values, discretization is necessary. Minimization entropy discretization method is used to discrete these input variables. After discretization, C4.5 decision tree algorithm is employed to determine the possible photo composition.
The experiments are conducted to test and analyze different compositions, such as sun-like composition, rule of thirds composition, horizontal composition and so on. The proposed method can correctly identify different photographic compositions, where the overall accuracy is 88.2%.

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