隨著科技的進步，數位相機與智慧型拍照手機逐漸普及化，拍照成為人們生活中不可或缺的一部分。該如何拍攝出美麗的照片是一門很重要的學問，需要花費一番苦心去學習，但並不是每個人都有時間去學習攝影技巧。
構圖是攝影中非常重要的一部分，對於照片的美學有一定的關連性。一個很好的構圖可以讓我們專注於攝影師想要在攝影主題中傳達的內容。為了讓幫助攝影的初學者學習如何拍攝構圖，在本篇論文中，我們研究了一套攝影構圖的指引方法。攝影的初學者能夠透過這個方法縮減學習拍攝構圖的時間。
在本篇論文中，我們使用卷積神經網路的分類系統並且加上邊緣特徵輔助預測結果。透過這個方法來做構圖的分類並將預測機率當作構圖的評分。最後利用層次序走訪的方法結合上述的構圖評分原則來找出指引的區域。
實驗中使用七種類型的構圖當作指引的對象。本文提出的方法可以有效地對影像的攝影構圖類型進行分類，召回率達95.0%以及精確率達94.8%。從實驗結果可以得知，被指引後的影像會更趨近於被指引的構圖類型。

With the progress of science and technology, digital cameras and smart phone gradually popularized, photography has become an indispensable part of people's lives. How to take a beautiful picture is a very important knowledge, and it takes a lot of time to learn, but not everyone has time to learn photography skills.
Composition is a very important part of photography and it has a certain relevance to the photography aesthetics. A good composition allows viewers to focus on the subject of the photography which the photographer wants to convey. In order to support photography beginners to learn how to take pictures with a good composition, we study a photographic composition guidance method in this paper. Photography beginners can shorten the time to learn photography composition.
In this paper, we use a convolutional neural network classification system and add edge features to support composition prediction. We use the training model to classify photography composition and the probability of prediction is used as the score of the composition. Finally, the level-order traversal method is used to find out the guidance region with the above scoring method.
Seven types of composition are used as guidelines in this experiment. The method proposed in this paper can effectively classify the photographic composition of images. The recall rate is 95.0% and the precision rate is 94.8%. It can be known from the experimental results that the guidance image will be closer to the type of guidance composition.

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