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Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/81525
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| Title: | 臉書相片分類及使用者樣貌分析
Identifying User Profile Using Facebook Photos. |
| Authors: | 張婷雅
Chang, Ting Ya |
| Contributors: | 廖文宏
Liao, Wen Hung
張婷雅
Chang,Ting Ya |
| Keywords: | 臉書
人臉偵測
環境識別
影像標籤
使用者樣貌分析
Facebook
face detection
scene understanding
image tag
user behavior analysis |
| Date: | 2016 |
| Issue Date: | 2016-03-01 10:40:21 (UTC+8) |
| Abstract: | 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。
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. |
| Reference: | [1] Viola, Paul, and Michael J. Jones. "Robust real-time face detection." International journal of computer vision 57.2 (2004): 137-154.
[2] ImageNet
http://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 |
| Description: | 碩士
國立政治大學
資訊科學學系
102753007 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0102753007 |
| Data Type: | thesis |
| Appears in Collections: | [資訊科學系] 學位論文
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Files in This Item:
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Size | Format | |
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| 300701.pdf | 5077Kb | Adobe PDF2 | 638 | View/Open |
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