請用此 Handle URI 來引用此文件：
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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 李琳山 | |
| dc.contributor.author | Hung-Tsung Lu | en |
| dc.contributor.author | 盧宏宗 | zh_TW |
| dc.date.accessioned | 2021-06-17T01:24:54Z | - |
| dc.date.available | 2017-08-10 | |
| dc.date.copyright | 2017-08-10 | |
| dc.date.issued | 2017 | |
| dc.date.submitted | 2017-08-08 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/67246 | - |
| dc.description.abstract | 本論文主要探討的是在加入使用者稀疏語音標註的條件下，進行個人相片語意檢索(semantic retrieval of personal photos)的方法。由於近年數位相機以及智慧型手機等行動裝置十分普及，使用者通常會快速累積大量的個人相片，如何有效率的對數量龐大的相片資料庫進行瀏覽與檢索是一個十分重要的問題。一般使用者通常偏好使用語意式查詢指令(semantic query)來對相片進行搜尋，例如：「母親節聚餐」。但機器基本上沒有能力掌握相片中攜帶的這類語意，除非使用者加以標註，因此我們設定在使用者拍攝相片的同時，可以使用麥克風針對相片進行語音標註；這樣會較鍵盤輸入文字便利許多，但使用者並不會將所有的照片皆加上語音標註。因此本論文將主題設定為使用者輸入稀疏語音標註條件下之個人相片語意檢索，意即僅有少數相片標上語音標註的情境。
本論文選擇使用以主題模型為基礎的實作方式，引入數種以深層學習為基礎的特徵抽取法，利用深層卷積式類神經網路對相片進行影像特徵抽取，同時使用分佈式詞彙表示法及段落向量模型針對相片語音標註的詞圖分別進行語音特徵抽取，最後以多模態深層自編碼器的主題模型，針對影像及語音特徵進行整合，並以此模型訓練出具備「潛藏主題」的瓶頸向量建構檢索模型。 此外，本論文亦將從主題模型檢索出的首次檢索結果(first-pass retrieval results)，使用字詞頻率期望值、局部與全域影像特徵、深層卷積式類神經網路影像特徵、段落向量與多模態自編碼器瓶頸特徵分別計算相片之間的相似度，再套用隨機漫步模型(random walk) 演算法，使相似度高的相片獲得相近的相關分數(relevance score)，進而達成重新排序的效果，使整體檢索效能更進一步的提 升。 最後，為使後續研究能夠更順利進行，本論文在最後將微軟COCO圖片標題資料庫加以中文化，並且將此中文版語料建檔，做為後續研究之用。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2021-06-17T01:24:54Z (GMT). No. of bitstreams: 1 ntu-106-R03922011-1.pdf: 5363834 bytes, checksum: 9810ec74cfc8a54811ef37fca289e2ee (MD5) Previous issue date: 2017 | en |
| dc.description.tableofcontents | 誌謝. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
中文摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii 一、導論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 研究動機. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 本論文研究方向. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 章節安排. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 二、背景知識. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1 語音辨識. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 檢索系統簡介. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.2.1 語音文件檢索. . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.2.2 以內容為基礎的影像檢索. . . . . . . . . . . . . . . . . . . . 11 2.3 主題模型. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.3.1 非負矩陣分解. . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.3.2 深層自編碼器. . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.3.3 多模態深層自編碼器. . . . . . . . . . . . . . . . . . . . . . . 16 2.4 圖論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.4.1 隨機漫步. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.5 詞彙表示法. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.5.1 傳統的詞彙表示法. . . . . . . . . . . . . . . . . . . . . . . . 19 2.5.2 分佈式詞彙表示法. . . . . . . . . . . . . . . . . . . . . . . . 20 2.6 文件表示法. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.6.1 傳統的文件表示法. . . . . . . . . . . . . . . . . . . . . . . . 22 2.6.2 分佈式文件表示法. . . . . . . . . . . . . . . . . . . . . . . . 23 2.7 以卷積式類神經網路為基礎之影像表示法. . . . . . . . . . . . . . . 24 三、以多模態深層自編碼器主題模型整合語音與影像特徵之相片語意檢索系統28 3.1 簡介. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.2 系統架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.3 影像特徵抽取. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.3.1 傳統影像特徵抽取方法. . . . . . . . . . . . . . . . . . . . . . 32 3.3.2 以深層學習為基礎之影像特徵抽取. . . . . . . . . . . . . . . 37 3.4 語音特徵抽取. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3.4.1 詞頻期望值語音特徵抽取. . . . . . . . . . . . . . . . . . . . 41 3.4.2 以深層學習為基礎之語音特徵抽取. . . . . . . . . . . . . . . 42 3.5 利用主題模型建構檢索模型. . . . . . . . . . . . . . . . . . . . . . . 43 3.5.1 以非負矩陣分解主題模型進行相片語意檢索. . . . . . . . . . 44 3.5.2 以多模態深層自編碼器主題模型進行相片語意檢索. . . . . . 46 3.6 實驗基礎設置. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 3.6.1 個人相片資料庫. . . . . . . . . . . . . . . . . . . . . . . . . . 51 3.6.2 個人相片語音標註收集過程. . . . . . . . . . . . . . . . . . . 51 3.6.3 語音辨識結果. . . . . . . . . . . . . . . . . . . . . . . . . . . 52 3.6.4 實驗配置. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 3.6.5 評估方式. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 3.7 實驗結果與分析. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 3.8 本章總結. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 四、以隨機漫步模型整合深度學習特徵增強個人相片語意檢索系統. . . . . . 57 4.1 簡介. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.2 系統架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 4.3 隨機漫步模型. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 4.3.1 裁剪. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.3.2 單層隨機漫步模型. . . . . . . . . . . . . . . . . . . . . . . . 59 4.3.3 雙層隨機漫步模型. . . . . . . . . . . . . . . . . . . . . . . . 61 4.4 實驗基礎設置. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.5 實驗結果與討論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 4.5.1 整體實驗結果. . . . . . . . . . . . . . . . . . . . . . . . . . . 65 4.6 本章總結. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 五、微軟COCO資料庫圖片標題中文化資料收集. . . . . . . . . . . . . . . . . 69 5.1 簡介. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 5.2 微軟COCO圖片標題資料庫. . . . . . . . . . . . . . . . . . . . . . . . 69 5.3 微軟COCO圖片標題資料庫之中文化. . . . . . . . . . . . . . . . . . 71 5.4 本章總結. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 六、結論與展望. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 6.1 結論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 6.2 未來研究方向. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 6.2.1 相片檢索系統語音辨識之改進. . . . . . . . . . . . . . . . . . 74 6.2.2 整合語音和影像特徵模型之改進. . . . . . . . . . . . . . . . 75 參考文獻. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 | |
| dc.language.iso | zh-TW | |
| dc.subject | 融合特徵 | zh_TW |
| dc.subject | 影像檢索 | zh_TW |
| dc.subject | 語音標註 | zh_TW |
| dc.subject | 分佈式詞彙表示法 | zh_TW |
| dc.subject | 段落向量 | zh_TW |
| dc.subject | 卷積式類神經網路 | zh_TW |
| dc.subject | 深層自編碼器 | zh_TW |
| dc.subject | 隨機漫步 | zh_TW |
| dc.subject | image retrieval | en |
| dc.subject | fused features | en |
| dc.subject | random walk | en |
| dc.subject | deep autoencoder | en |
| dc.subject | convolutional neural network | en |
| dc.subject | paragraph vector | en |
| dc.subject | distributed word representation | en |
| dc.subject | speech annotation | en |
| dc.title | 使用多模態深層自編碼器融合視覺與語音特徵強化個人相片之語意檢索 | zh_TW |
| dc.title | Semantic Retrieval of Personal Photos Using Multimodal Deep Autoencoder Fusing Visual and Speech Features | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 105-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 王小川,鄭秋豫,陳信宏,李宏毅 | |
| dc.subject.keyword | 影像檢索,語音標註,分佈式詞彙表示法,段落向量,卷積式類神經網路,深層自編碼器,隨機漫步,融合特徵, | zh_TW |
| dc.subject.keyword | image retrieval,speech annotation,distributed word representation,paragraph vector,convolutional neural network,deep autoencoder,random walk,fused features, | en |
| dc.relation.page | 84 | |
| dc.identifier.doi | 10.6342/NTU201700762 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2017-08-08 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-106-1.pdf 未授權公開取用 | 5.24 MB | Adobe PDF |
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