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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 徐宏民(Winston H. Hsu) | |
| dc.contributor.author | Yen-Ta Huang | en |
| dc.contributor.author | 黃彥達 | zh_TW |
| dc.date.accessioned | 2021-06-16T17:22:04Z | - |
| dc.date.available | 2017-08-28 | |
| dc.date.copyright | 2012-08-28 | |
| dc.date.issued | 2012 | |
| dc.date.submitted | 2012-08-16 | |
| dc.identifier.citation | [1] S. Ahern, M. Naaman, R. Nair, and J. H.-I. Yang. World explorer: visualizing
aggregate data from unstructured text in geo-referenced collections. In Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries, JCDL ’07, pages 1–10, New York, NY, USA, 2007. ACM. [2] R. B. et al. Electronic compass and compensation of large magnetic errors for operation over all orientations. US patent 6543146 on 8 Apr 2003. [3] D. Comaniciu and P. Meer. Mean shift: a robust approach toward feature space analysis. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(5):603 –619, may 2002. [4] M. Cooper, J. Foote, A. Girgensohn, and L. Wilcox. Temporal event clustering for digital photo collections. ACM Trans. Multimedia Comput. Commun. Appl., 1(3):269–288, Aug. 2005. [5] D. J. Crandall, L. Backstrom, D. Huttenlocher, and J. Kleinberg. Mapping the world’s photos. In Proceedings of the 18th international conference on World wide web, pages 761–770, New York, NY, USA, 2009. ACM. [6] G. Csurka, C. R. Dance, L. Fan, J. Willamowski, and C. Bray. Visual categorization with bags of keypoints. In In Workshop on Statistical Learning in Computer Vision, ECCV, pages 1–22, 2004. [7] A. R. Dick, P. H. S. Torr, and R. Cipolla. Modelling and interpretation of architecture from several images. Int. J. Comput. Vision, 60(2):111–134, Nov. 2004. [8] Y. Gao, J. Tang, R. Hong, Q. Dai, T.-S. Chua, and R. Jain. W2go: a travel guidance system by automatic landmark ranking. In Proceedings of the international conference on Multimedia, MM ’10, pages 123–132. ACM, 2010. [9] E. Gavves and C. G. Snoek. Landmark image retrieval using visual synonyms. In Proceedings of the international conference on Multimedia, MM ’10, pages 1123–1126, New York, NY, USA, 2010. ACM. [10] J. Hays and A. A. Efros. Scene completion using millions of photographs. ACM Transactions on Graphics (SIGGRAPH 2007), 26(3), 2007. [11] J. Hays and A. A. Efros. im2gps: estimating geographic information from a single image. In Proceedings of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2008. [12] Y.-H. Kuo, W.-Y. Lee, W. H. Hsu, and W.-H. Cheng. Augmenting mobile cityview image retrieval with context-rich user-contributed photos. In Proceedings of the 19th ACM international conference on Multimedia, MM ’11, pages 687– 690, New York, NY, USA, 2011. ACM. [13] D. G. Lowe. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision, 60(2):91–110, Nov. 2004. [14] M. Naaman, A. Paepcke, and H. Garcia-Molina. From where to what: Metadata sharing for digital photographs with geographic coordinates. In 11th International Conference on Cooperative Information Systems (COOPIS 2003), November 2003. [15] M. Park, J. Luo, R. T. Collins, and Y. Liu. Beyond gps: determining the camera viewing direction of a geotagged image. In Proceedings of the international conference on Multimedia, MM ’10, pages 631–634. ACM, 2010. [16] J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman. Object retrieval with large vocabularies and fast spatial matching. In CVPR 2007, pages 1 –8, june 2007. [17] T. Rattenbury, N. Good, and M. Naaman. Towards automatic extraction of event and place semantics from flickr tags. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR ’07, pages 103–110, New York, NY, USA, 2007. ACM. [18] I. Simon, N. Snavely, and S. Seitz. Scene summarization for online image collections. In Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on, pages 1 –8, oct. 2007. [19] V. K. Singh, M. Gao, and R. Jain. Social pixels: genesis and evaluation. In Proceedings of the international conference on Multimedia, MM ’10, pages 481– 490, New York, NY, USA, 2010. ACM. [20] N. Snavely, S. M. Seitz, and R. Szeliski. Photo tourism: exploring photo collections in 3d. ACM Trans. Graph., 25(3):835–846, July 2006. [21] H.-H. Su, T.-W. Chen, C.-C. Kao, W. H. Hsu, and S.-Y. Chien. Scenic photo quality assessment with bag of aesthetics-preserving features. In Proceedings of the 19th ACM international conference on Multimedia, MM ’11, pages 1213– 1216, New York, NY, USA, 2011. ACM. [22] Y.-T. Zheng, M. Zhao, Y. Song, H. Adam, U. Buddemeier, A. Bissacco, F. Brucher, T.-S. Chua, and H. Neven. Tour the world: Building a web-scale landmark recognition engine. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pages 1085 –1092, june 2009. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/63888 | - |
| dc.description.abstract | 我們去估計網路上所分享的照片的拍攝角度，藉此去找出各個景點或地標之不同角度的景色。首先利用照片本身附帶的文字標籤，我們可以找出景點的範圍以及它的位置。利用照片本身附帶的地理位置標籤以及找出的景點位置，我們就可以估算照片拍攝的角度。接著利用照片的EXIF資訊再確認照片的拍攝目標是這個景點，以此再過濾之前的結果。此外，如果我們要找的地標範圍很大，則我們會進一步找出這個範圍裡有哪些小景點，然後藉由這些小景點做出各種角度照片的推薦。
在我們的實驗，這個估算位置的方法誤差大約在30公尺，再以此找出照片拍攝角度，實驗証明這樣的方法推算出的角度較其他的方法準確，誤差約在14度以內。利用這些推算出的角度，以及考慮照片的美學，我們便可以推薦景點各個角度比較好看的照片。此外因為整個方法的不需要大量的運算，因此很適合運用在即時的推薦系統上。 | zh_TW |
| dc.description.abstract | We propose to leverage the geo-tagged photos crawled from social-sharing websites to display the various viewing directions to landmarks. First, we predict the location and the boundaries of a landmark by the user-contributed tags of photos. The viewing directions of the photos annotated as the landmark are from their geo-locations to the predicted location of the landmark. To ensure the shooting target is the landmark, we use the camera information embedded in EXIF, and refine the results. Besides, if the landmark has large boundaries, we further discover the attractions within its boundaries. And make a summary by these attractions.
In our experiments, we show that the deviation of the estimated location is about 30 meters, and we demonstrate the average error to the real viewing directions is less than 14$^{circ}$. The estimated viewing directions enable illustrating landmarks from different views and recommending users the popular views for taking photos on the landmarks. This proposed framework is efficient enough to be applied onto million-scale photos, and serve as a recommendation system. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T17:22:04Z (GMT). No. of bitstreams: 1 ntu-101-R99922006-1.pdf: 16892113 bytes, checksum: 1aebb9d43c369a50ed18cb4b17fc35c7 (MD5) Previous issue date: 2012 | en |
| dc.description.tableofcontents | 致謝i
摘要ii Abstract iii 1 Introduction 1 2 Related Work 3 3 System Overview 5 3.1 Concept Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 4 Dataset Collection 8 5 Estimating the Locations of Landmarks 9 6 Discovering the Attractions 11 7 Estimating the Viewing Directions 13 7.1 Verifying Photo Contents . . . . . . . . . . . . . . . . . . . . . . . . 13 7.2 Photo Aesthetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 7.3 Refining with Focal Length . . . . . . . . . . . . . . . . . . . . . . . 14 7.4 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 8 Experiments and Evaluations 16 8.1 Boundaries of the Designated Landmark . . . . . . . . . . . . . . . . 16 8.2 Estimated Location of the Target . . . . . . . . . . . . . . . . . . . . 18 8.3 Viewing Directions of Photos . . . . . . . . . . . . . . . . . . . . . . 19 8.4 Effectiveness of Attraction Discovery . . . . . . . . . . . . . . . . . . 20 8.5 Efficiency of the Refinement by Focal Lennth . . . . . . . . . . . . . 22 9 Conclusions 25 Bibliography 26 | |
| dc.language.iso | zh-TW | |
| dc.subject | 地標搜尋 | zh_TW |
| dc.subject | 地理位置資訊 | zh_TW |
| dc.subject | 照片拍攝角度 | zh_TW |
| dc.subject | 景點範圍 | zh_TW |
| dc.subject | 區域分析 | zh_TW |
| dc.subject | Geo-information | en |
| dc.subject | Region-based | en |
| dc.subject | Landmark | en |
| dc.subject | Attraction Discover | en |
| dc.subject | Viewing Direction | en |
| dc.subject | Boundary | en |
| dc.title | 分析網路使用者分享的大量照片以提供各種地標之不同角度影像搜尋與推薦 | zh_TW |
| dc.title | Detecting Various Viewing Directions to Landmarks for Recommendation by Large-Scale User-Contributed Photos | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 100-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳良弼(Arbee L.P. Chen),劉庭祿,林軒田(Hsuan-Tien Lin) | |
| dc.subject.keyword | 區域分析,地標搜尋,照片拍攝角度,地理位置資訊,景點範圍, | zh_TW |
| dc.subject.keyword | Region-based,Landmark,Attraction Discover,Viewing Direction,Boundary,Geo-information, | en |
| dc.relation.page | 28 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2012-08-17 | |
| 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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| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-101-1.pdf 未授權公開取用 | 16.5 MB | Adobe PDF |
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