本論文提供一種新的互動式圖像編輯方法：根據「性質」來調整圖像。目前許多與類型神經網路相關的圖像編輯方法常要求使用者具備相當的圖像編輯熟悉程度與技巧，以修改指定區塊的範圍和內容。為了降低實作圖像編輯的複雜程度與加快執行效率，我們提供了一個構造簡單卻結構完整的圖像編輯系統，以該系統來有效地實作兩種精細的編輯方法。第一種是使用「區塊大小」的性質實作「邊界調整」，第二種則是使用「顏色」性質實作「色調調整」。
我們使用深層卷積類神經網路來切割圖像分成數個區塊，每個區塊都有上述提及的性質資訊。在「邊界調整」方法裡，我們使用「影像細縫裁減」方法調整特定區塊的大小。在「色調調整」方法中我們根據事先準備的圖像資料庫中取出其中一筆顏色性質的資料，根據其資料修改指定區塊的色調。實驗結果顯示我們提出的方法能夠快速且有效地完成圖像編輯，產生出逼真的修改成果。

This thesis presents a novel concept of interactive image editing: to edit an image by its attributes. We aim to alleviate the difficulty of conventional image editing
methods, which often require the user to be experienced and skillful in manipulating the regions and contents of an image. We introduce an attribute-based image editing
framework, and demonstrate two plausible editing tasks that can be effectively done using our framework. The first one is boundary adjustment with respect to the ‘semantic region size’ attribute and the second one is color transfer with respect to the ‘natural color’ attribute. We use the Fully Convolutional Network (FCN) model to segment a given image into several semantic regions and then characterize each region by the aforementioned attributes. For the boundary adjustment task, we adopt the seam carving method to adjust the semantic region size attribute of the selected region, and therefore the user is allowed to change the composition of the image interactively. For the color transfer task, we model the natural color attribute of the selected region by referring to the distribution of color attributes in the database. The experimental results show that our method is efficient and easy to generate visually pleasing editing results.

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