| dc.contributor.advisor | 廖文宏 | zh_TW |
| dc.contributor.advisor | Liao, Wen Hung | en_US |
| dc.contributor.author (Authors) | 張婷雅 | zh_TW |
| dc.contributor.author (Authors) | Chang,Ting Ya | en_US |
| dc.creator (作者) | 張婷雅 | zh_TW |
| dc.creator (作者) | Chang, Ting Ya | en_US |
| dc.date (日期) | 2016 | en_US |
| dc.date.accessioned | 1-Mar-2016 10:40:21 (UTC+8) | - |
| dc.date.available | 1-Mar-2016 10:40:21 (UTC+8) | - |
| dc.date.issued (上傳時間) | 1-Mar-2016 10:40:21 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0102753007 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/81525 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 資訊科學學系 | zh_TW |
| dc.description (描述) | 102753007 | zh_TW |
| dc.description.abstract (摘要) | 除了文字訊息，張貼相片也是臉書使用者常用的功能，這些上傳的照片種類繁多，可能是自拍照、風景照、或食物照等等，本論文的研究以影像分析為出發點，探討相片內容跟發佈者間之關係，希望藉由相片獲得的資訊，輔助分析使用者樣貌。本研究共收集32位受測者上傳至臉書的相片，利用電腦視覺技術分析圖像內容，如人臉偵測、環境識別、找出影像上視覺顯著的區域等，藉由這些工具所提供的資訊，將照片加註標籤，以及進行自動分類，並以此兩個層次的資訊做為特徵向量，利用階層式演算法進行使用者分群，再根據實驗結果去分析每一群的行為特性。透過此研究，可對使用者進行初步分類、瞭解不同的使用者樣貌，並嘗試回應相關問題，如使用者所張貼之相片種類統計、不同性別使用者的上傳行為、 依據上傳圖像內容，進行使用者樣貌分類等，深化我們對於臉書相片上傳行為的理解。 | zh_TW |
| dc.description.abstract (摘要) | Apart from text messages, photo posting is a popular function of Facebook. The uploaded photos are of various nature, including selfie, outdoor scenes, and food. In this thesis, we employ state-of-the-art computer vision techniques to analyze image content and establish the relationship between user profile and the type of photos posted.We collected photos from 32 Facebook users. We then applied techniques such as face detection, scene understanding and saliency map identification to gather information for automatic image tagging and classification. Grouping of users can be achieved either by tag statistics or photo classes. Characteristics of each group can be further investigated based on the results of hierarchical clustering. We wish to identify profiles of different users and respond to questions such as the type of photos most frequently posted, gender differentiation in photo posting behavior and user classification according to image content, which will promote our understanding of photo uploading activities on Facebook. | en_US |
| dc.description.tableofcontents | 第一章 緒論 11.1 研究背景與目的 11.2 流程架構與方法 31.3 論文貢獻 41.4 論文架構 4第二章 相關研究與技術背景 62.1 文獻探討 62.2 研究工具及技術背景介紹 92.2.1 Facebook相片蒐集 92.2.2 Face++ 102.2.3 Rekognition 112.2.4 Saliency map 122.2.5 階層式分群演算法 12第三章 研究方法 163.1 定義相片的類別 163.1.1 人物照 163.1.2 景物照 173.1.3 主題照 183.1.4 非寫實照 193.1.5食物照 203.1.6 動物照 213.1.7 文字照 223.1.8 其他 233.2 圖像分類方法研究 243.2.1 人物照 243.2.2 景物照 273.2.3 主題照 293.2.4 非寫實照 303.2.5 食物照 323.2.6 動物照 333.2.7 文字照 333.3 標籤探討 353.3.1 過濾 353.3.2 考慮權重 363.3.3 合併意義相似的標籤項目 39第四章 實驗結果與討論 414.1 使用者所張貼之相片，以哪種種類的相片最多? 434.2 男性、女性所張貼的相片內容，是否有所差異? 524.3如何依據資料內容，進行使用者樣貌分析? 554.3.1 相片種類樣貌分析 554.3.2 人物照樣貌分析 584.3.3 標籤樣貌分析 604.4用標籤將使用者分群是否會和用照片分類結果將使用者分群的結果一致? 624.5對於現有的資料，有何其他相關的應用? 64第五章 結論與未來研究方向 70參考文獻 72附錄 74 | zh_TW |
| dc.format.extent | 5198915 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0102753007 | en_US |
| dc.subject (關鍵詞) | 臉書 | zh_TW |
| dc.subject (關鍵詞) | 人臉偵測 | zh_TW |
| dc.subject (關鍵詞) | 環境識別 | zh_TW |
| dc.subject (關鍵詞) | 影像標籤 | zh_TW |
| dc.subject (關鍵詞) | 使用者樣貌分析 | zh_TW |
| dc.subject (關鍵詞) | Facebook | en_US |
| dc.subject (關鍵詞) | face detection | en_US |
| dc.subject (關鍵詞) | scene understanding | en_US |
| dc.subject (關鍵詞) | image tag | en_US |
| dc.subject (關鍵詞) | user behavior analysis | en_US |
| dc.title (題名) | 臉書相片分類及使用者樣貌分析 | zh_TW |
| dc.title (題名) | Identifying User Profile Using Facebook Photos. | en_US |
| dc.type (資料類型) | thesis | en_US |
| dc.relation.reference (參考文獻) | [1] Viola, Paul, and Michael J. Jones. "Robust real-time face detection." International journal of computer vision 57.2 (2004): 137-154.[2] ImageNethttp://image-net.org/.[3] Russakovsky, Olga, et al. "Imagenet large scale visual recognition challenge." arXiv preprint arXiv:1409.0575 (2014).[4] Vinyals, Oriol, et al. "Show and tell: A neural image caption generator." arXiv preprint arXiv:1411.4555 (2014).[5] Hu, Yuheng, Lydia Manikonda, and Subbarao Kambhampati. "What we instagram: A first analysis of instagram photo content and user types." Proceedings of ICWSM. AAAI (2014).[6] Ensky’s Album Downloader for Facebook,https://sofree.cc/download-fb-album-photo/.[7] Face++,http://www.faceplusplus.com/.[8] Rekognition,https://rekognition.com/.[9] Itti, Laurent, Christof Koch, and Ernst Niebur. "A model of saliency-based visual attention for rapid scene analysis." IEEE Transactions on Pattern Analysis & Machine Intelligence 11 (1998): 1254-1259.[10] Cheng, Ming, et al. "Global contrast based salient region detection." Pattern Analysis and Machine Intelligence, IEEE Transactions on 37.3 (2015): 569-582.[11] Perazzi, Federico, et al. "Saliency filters: Contrast based filtering for salient region detection." Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. IEEE, 2012.[12] Team, R. Core. "R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2012." (2014).https://www.r-project.org/.[13] 階層式分群法(Hierarchical Clustering),http://goo.gl/mDfDp.[14] 蔣佳欣 (2006)，室內/戶外與建築物/自然風景之影像分類研究，碩士論文，南台科技大學資訊工程所，臺南。[15] Kawano, Yoshiyuki, and Keiji Yanai. "FoodCam: A Real-Time Mobile Food Recognition System Employing Fisher Vector." MultiMedia Modeling. Springer International Publishing, 2014.[16] Zhang, Weiwei, Jian Sun, and Xiaoou Tang. "Cat head detection-how to effectively exploit shape and texture features." Computer Vision–ECCV 2008. Springer Berlin Heidelberg, 2008. 802-816.[17] 王石番（1991），《傳播內容分析法》，幼獅 | zh_TW |
