請用此 Handle URI 來引用此文件:
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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 徐宏民(Winston H. Hsu) | |
dc.contributor.author | Yin-Ying Chen (A.K.A. Yan-Ying Chen) | en |
dc.contributor.author | 陳殷盈 | zh_TW |
dc.date.accessioned | 2021-05-15T17:52:30Z | - |
dc.date.available | 2017-09-04 | |
dc.date.available | 2021-05-15T17:52:30Z | - |
dc.date.copyright | 2014-09-04 | |
dc.date.issued | 2014 | |
dc.date.submitted | 2014-08-11 | |
dc.identifier.citation | Bibliography
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/5142 | - |
dc.description.abstract | 數量持續成長的社群媒體用戶基於共享和社交的目的,貢獻大量人物照片。這些人物多媒體資料(如旅行照片和家人影片等)保有豐富的人群活動資料,對行動推薦系統,個人化,廣告和更多以人為中心的應用非常有利。有鑒於這些強烈需求,我們提出利用影像中自動偵測獲得的人物訊息(如人臉屬性,人物群體類別,視覺情感概念)來幫助社交多媒體分析。本計畫進一步結合計算社會學和認知心理學來了解社群使用者所提供的視覺資料中所挖掘出的知識訊息。最後,我們並展示利用百萬規模的社群影像及其周邊資訊(如地理位置,時間,標籤和評論)來幫住人物特徵分析,人口統計調查和社群情感運算。就我們所知,這是第一個利用大規模社交視覺資料來幫助分析使用者行為的研究工作。 | zh_TW |
dc.description.abstract | A growing population of the Internet users are contributing a huge amount of photos and videos to social media for the purpose of sharing and social communication. These big human-centric media collections such as travel photos and family videos retain abundant people activities inherently beneficial for mobile recommender system, personalization, advertisement and more people-related applications. Witnessing these strong needs, we propose to exploit the human-centric contexts automatically detected from visual content, e.g., people attributes, social group types and visual concepts, for social multimedia analytics. The proposed approach further incorporates computational sociology and cognitive psychology to understand the knowledge mined from the visual content contributed by real users. Finally, we demonstrate its effectiveness for user profiling, demographic investigation and social affective computing by using million-scale social images and the associated metadata (i.e., geo-locations, time stamps, tags and comments) crawled from social media. To the best of our knowledge, this is the first work addressing how large-scale visual contexts can help user profiling and improve user behavior analysis. | en |
dc.description.provenance | Made available in DSpace on 2021-05-15T17:52:30Z (GMT). No. of bitstreams: 1 ntu-103-D98922014-1.pdf: 12739852 bytes, checksum: 81634e574fae63909bb4fab31285dcdf (MD5) Previous issue date: 2014 | en |
dc.description.tableofcontents | 摘要iii
Abstract v 1 Introduction 1 2 Literature Review 5 3 Learning facial attributes by weakly labeled images 7 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.2 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.3 System Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.4 Selecting Effective Features from Noisily Labeled Images . . . . . . . . 14 3.4.1 Harvesting Training Image Candidates . . . . . . . . . . . . . . . 14 3.4.2 Extracting Multiple Visual Feature Combinations . . . . . . . . . 15 3.4.3 Computing Textual Relevance . . . . . . . . . . . . . . . . . . . 15 3.4.4 Measuring Feature Quality by Discriminability Voting . . . . . . 16 3.4.5 Optimizing Feature Set . . . . . . . . . . . . . . . . . . . . . . . 18 3.5 Measuring Annotation Quality for Determining Effective Training Images 20 3.5.1 Measuring Visual Relevance . . . . . . . . . . . . . . . . . . . . 21 3.5.2 Combining Textual and Visual Relevance . . . . . . . . . . . . . 21 3.5.3 Considering Geo-locations . . . . . . . . . . . . . . . . . . . . . 22 3.6 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.6.1 Threshold Selection . . . . . . . . . . . . . . . . . . . . . . . . 24 3.6.2 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.6.3 Effects of Geo-Context . . . . . . . . . . . . . . . . . . . . . . . 33 3.7 Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.8 Extensive Applications: Retrieving Images by Facial Attributes . . . . . 35 3.8.1 Face Image Retrieval using Attribute-Enhanced Sparse Codewords 35 3.8.2 Face Image Retrieval by Facial Attributes and Canvas Layout . . 35 4 Mining facial attributes and social relationships 37 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 4.2 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 4.3 Building a Vocabulary of Facial Subgraphs . . . . . . . . . . . . . . . . 42 4.3.1 Graph Construction . . . . . . . . . . . . . . . . . . . . . . . . . 43 4.3.2 Enumeration of Subgraphs . . . . . . . . . . . . . . . . . . . . . 45 4.4 Bag-of-Face-Subgraphs . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 4.4.1 Subgraph Selection . . . . . . . . . . . . . . . . . . . . . . . . . 47 4.4.2 Feature Representation of Group Photos . . . . . . . . . . . . . . 48 4.5 Predicting Pairwise Relationships . . . . . . . . . . . . . . . . . . . . . 50 4.6 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4.6.1 Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4.6.2 Effects from Learning Approaches . . . . . . . . . . . . . . . . . 53 4.6.3 Mined Informative Subgraphs for Family . . . . . . . . . . . . . 54 4.6.4 Sensitivity in Pixel vs. Order Distance . . . . . . . . . . . . . . . 55 4.6.5 Effects of Subgraph Selection . . . . . . . . . . . . . . . . . . . 56 4.6.6 Performance of Predicting Pairwise Relationships . . . . . . . . . 57 4.7 Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 4.8 Extensive Applications: Personalized and Group Recommendation for Tourism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.8.1 Personalized Travel Recommendation . . . . . . . . . . . . . . . 59 4.8.2 Group Recommendation . . . . . . . . . . . . . . . . . . . . . . 60 5 Predicting Affective Comments for Images in Social Media 61 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 5.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 5.3 Viewer Affect Concept Discovery . . . . . . . . . . . . . . . . . . . . . 65 5.4 Publisher-Viewer Affect Correlation . . . . . . . . . . . . . . . . . . . . 66 5.4.1 Publisher Affect Concepts . . . . . . . . . . . . . . . . . . . . . 67 5.4.2 Bayes Probabilistic Correlation Model . . . . . . . . . . . . . . . 68 5.4.3 Smoothing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 5.5 Applications and Experiments . . . . . . . . . . . . . . . . . . . . . . . 71 5.5.1 Dataset for Mining and Evaluation . . . . . . . . . . . . . . . . . 71 5.5.2 Image Recommendation for Target Affect Concepts . . . . . . . . 72 5.5.3 Evoked Viewer Affect Concept Prediction . . . . . . . . . . . . . 74 5.5.4 Automatic Commenting Assistant . . . . . . . . . . . . . . . . . 75 5.6 Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 6 Conclusions and Future Work 81 Bibliography 83 | |
dc.language.iso | en | |
dc.title | 基於使用者生成多媒體內容之巨量資料分析 | zh_TW |
dc.title | Human-Centric Data Analytics from User-Contributed Media Collections | en |
dc.type | Thesis | |
dc.date.schoolyear | 102-2 | |
dc.description.degree | 博士 | |
dc.contributor.coadvisor | 廖弘源(Hong-Yuan Mark Liao) | |
dc.contributor.oralexamcommittee | 曾新穆(Vincent S. Tseng),王蒞君(Li-Chun Wang),莊仁輝(Jen-Hui Chuang),賴尚宏(Shang-Hong Lai),歐陽明(Ming Ouhyoung) | |
dc.subject.keyword | 巨量資料分析,多媒體檢索與探勘,情感計算, | zh_TW |
dc.subject.keyword | Big Data Analytics,Multimedia Retrieval and Mining,Affective Computing, | en |
dc.relation.page | 91 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2014-08-11 | |
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 |
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