旅館場景影像自動分類系統

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姓名

林翰廷(Han-Ting Lin) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

旅館場景影像自動分類系統
(Automatic Hotel scene image classification system)

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至系統瀏覽論文 (2024-7-26以後開放)

摘要(中)

場景識別是圖像語義分割中相當重要的一個環節，而如何正確且有效率地在場景中找到有效資訊的位置，是場景識別領域中十分困難的問題。在場景識別的任務上，場景是由物體、空間布局和背景之間的關聯關係等因素綜合而成的，而場景中的物體種類對分類結果影響甚深，透過辨識的場景物體分類出場景，例如浴室中的浴缸或馬桶、臥室中的床或書桌等。

本論文提出的方法是以辨識物體的特徵作為前處理的步驟，再根據結果分類出特定場景，透過Mask R-CNN算法針對輸入的圖片進行特定室內物件分割的處理，接著以分割完的物件作為場景的特徵，再與場景結合並進行分類。實驗結果證明，透過獲取場景中物件特徵的方法的前處理，能在場景識別中取得更好的場景分類準確度。

摘要(英)

Scene Recognition is an important operation of Image Semantic Segmentation, in the wide range of scene recognition, it is a thorny issue to correctly and efficient find effective location information in specific scene. In the mission of scene recognition, a scene is mainly comprised of three elements, including object, spatial layout and the relationship between backgrounds, these object types in scene have huge impact on results of classification. Through this matter, scene could be recognized based on those identified objects of scene, for example, bathtub or toilet in the bathroom, bed or writing desk in the bedroom.
In this thesis, an effective architecture for scene recognition is proposed. The architecture includes a pre-process step to identify feature of each object, then classify specified scene based on the results of object feature. Moreover, those input pictures will be pre-processed through Mask R-CNN algorithm to identify specific indoor objects by results of segmentation, and those specified indoor objects become elements for scene recognition classification. The experimental results show that through pre-process of object identification, the proposed method has the advantages of accuracy in scene recognition.

關鍵字(中)

★ 室內場景
★ VGG16
★ Mask R-CNN
★ 特徵融合

關鍵字(英)

論文目次

摘要 I
Abstract II
目錄 III
圖目錄 IV
表目錄 V
第一章緒論 1
1.1 研究動機 1
1.2 研究背景 2
1.3 系統架構 4
第二章相關文獻 5
2.1 取得物體特徵 5
2.2.1 物體偵測 6
2.2.2 語義分割 6
2.2 神經網路架構 7
2.2.1 VGG-16 7
2.2.2 Inception 8
2.3優化器 12
2.4 防止過擬合 13
第三章研究方法與系統程式 13
3.1 資料集 14
3.2 物件分割與預處理 16
3.2.1 Mask R-CNN與instance segmentation 16
3.2.2 Mask物件的顏色代表圖 17
3.3 建立特徵提取網路 20
3.2.1 參數設置 20
3.2.2 網路架構與特徵提取 20
3.4 特徵融合 21
3.5 系統程式 22
第四章實驗結果 24
4.1 不同網路架構 24
4.2 提取特徵與融合的方式 26
第五章結論與未來研究方向 27
參考文獻 28

參考文獻

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指導教授

鄭旭詠

審核日期

2019-7-26

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