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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 鄭卜壬 | |
dc.contributor.author | Shao-Hang Kao | en |
dc.contributor.author | 高紹航 | zh_TW |
dc.date.accessioned | 2021-06-15T06:17:16Z | - |
dc.date.available | 2011-08-16 | |
dc.date.copyright | 2010-08-16 | |
dc.date.issued | 2010 | |
dc.date.submitted | 2010-08-11 | |
dc.identifier.citation | [1] A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain. Content-Based Image Retrieval at the End of the Early Years. In IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(12) 1349-1380, 2000.
[2] Canny J.: A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8, 6 (1986), 679–698. [3] Carlo Tomasi, Roberto Manduchi, Bilateral Filtering for Gray and Color Images, proceedings of the ICCV 1998 [4] C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender. Learning to rank using gradient descent. In Proceedings of the International Conference on Machine Learning, 2005. [5] Chang C.-C., Lin C.-J.: LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/ cjlin/libsvm. [6] Che-Hua Yeh, Wai-Seng Ng, Brian A. Barsky and Ming Ouhyoung, An esthetics rule-based ranking system for amateur photos, ACM SIGGRAPH ASIA 2009 Sketches. [7] Cohen-Or D., Sorkine O., Gal R., Leyvand T., XU Y.-Q.: Color harmonization. 624–630. (SIGGRAPH 2006 Conference Proceedings). [8] Daugman, J.G., 'Two-dimensional spectral analysis of cortical receptive field profiles', Vision Res. 20 (10): 847–56 [9] D. Cai, X. He, Z. Li, W.-Y. Ma, and J.-R. Wen. Hierarchical clustering of WWW image search results using visual, textual and link information. In Proceedings of the 12th annual ACM international conference on Multimedia, pages 952-959, New York, 2004. [10] DPChallenge, http://www.dpchallenge.com/ [11] DUDA R. O., HART P. E.: Use of the hough transformation to detect lines and curves in pictures. Communications of the ACM 15, 1 (1972), 11–15. [12] Flickr, http://www.flickr.com/ [13] Google Image Search, http://images.google.com/ [14] H. Liu, X. Xie, X. Tang, Z.-W. Li, and W.-Y. Ma. Effective browsing of web image search results. In Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval, pages 84-90, 2004. [15] H. Tong, M. Li, H.-J. Zhang, J. He, and C. Zhang. Classification of Digital Photos Taken by Photographers or Home Users. In Proc. Pacific-Rim Conference on Multimedia, 2004. [16] INRIA dataset, http://lear.inrialpes.fr/~jegou/data.php [17] ITTI L., KOCH C., NIEBUR E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 11 (1998), 1254–1259. [18] Kendall, M. 'A New Measure of Rank Correlation'. Biometrika 30 (1-2): 81–89. [19] O. Chapelle, D. Metlzer, Y. Zhang and P. Grinspan: Expected reciprocal rank for graded relevance. In Proceeding of the 18th ACM conference on Information and knowledge management, 2009. [20] Photo.net, http://photo.net/ [21] Phuc-Tue Le Dinh and Jacques Patry. Video compression artifacts and MPEG noise reduction. Video Imaging DesignLine. February 24, 2006. [22] Qi Shan, Jiaya Jia, and Aseem Agarwala, High-quality Motion Deblurring from a Single Image, ACM SIGGRAPH 2008 Papers. [23] Ray, Sidney F. 2000. Camera Exposure Determination. In The Manual of Photography: Photographic and Digital Imaging, 9th ed. Ed. Ralph E. Jacobson, Sidney F. Ray, Geoffrey G. Atteridge, and Norman R. Axford. Oxford: Focal Press. ISBN 0-240-51574-9 [24] R. Datta, J. Li, and J. Z. Wang. Content-based image retrieval: approaches and trends of the new age. In Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval, pages 253-262, 2005. [25] Soonmin Bae, Sylvain Paris and Fredo Durand, Two-scale tone management for photographic look, ACM SIGGRAPH 2006 Papers. [26] Tong H., Li M., Zhang H.-J., Zhang C.: Blur detection for digital images using wavelet transform. In Proceedings of the 2004 IEEE International Conference on Multimedia and Expo (2004), pp. 17–20. [27] Y. Ke, X. Tang, and F. Jing. The Design of High-Level Features for Photo Quality Assessment. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 419-426, 2006. [28] Yushi Jing and Shumeet Baluja. PageRank for Product Image Search. In Prof. of International World Wide Web Conference, 2008. [29] Y. W. Chen, C. J. Lin, Combining SVMs with various feature selection strategies, In Feature extraction, foundations and applications (2006). | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/47765 | - |
dc.description.abstract | 在這篇論文中,我們提出了將現有圖片搜尋結果基於圖片品質重新排序的方法。較早期的研究中大多專注於圖片搜尋結果與查詢字的相關性,而導致有些在搜尋結果中排序前面的圖片其視覺上的品質太差無法滿足使用者。為了改善此問題,我們利用機器學習的方法從圖片中抽取特徵,並根據其與圖片品質的關係建立回歸模型,並利用此回歸模型之預測值對圖片做排序。排序結果可提升使用者對於搜尋結果的滿意度。除此之外我們也提出了不同於傳統圖片特徵的新特徵,並且經實驗證明能夠有效的提升結果。我們將實驗做在來自Google, Flickr, INRIA, Photo.net以及DPChallenge的圖片上。並且建立了一個線上圖片搜尋系統,用來將Google以及Flickr的搜尋結果重新排序後呈現給使用者。實驗結果顯示我們的方法在美學的標準下的確提升了圖片搜尋的效果。 | zh_TW |
dc.description.abstract | We propose an approach to re-rank images retrieved from existing image search engines based on image quality in aesthetic view. Previous works ranked images mainly based on their relevance to queries. However, it happens that often the top-ranked images cannot satisfy users due to their low visual quality. To present quality images to users, our approach learns a regression model, which combines both conventional and novel image features according to a given quality-image collection. Several experiments have been conducted on the datasets sampled from INRIA Holiday datasets , Photo.net , DPChallenge , Google Image , and Flickr . The experimental results show the feasibility of the proposed approach in searching aesthetically-pleasing Web-image search results for users. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T06:17:16Z (GMT). No. of bitstreams: 1 ntu-99-R97922009-1.pdf: 4451407 bytes, checksum: fc3a9735e54981d1f099c4ddd196f140 (MD5) Previous issue date: 2010 | en |
dc.description.tableofcontents | 口試委員會審定書 #
誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vii LIST OF TABLES ix Chapter 1 Introduction 1 Chapter 2 Related Work 4 2.1 Web Image Search 4 2.2 Image Quality 4 2.3 Image Refinement 5 Chapter 3 The Proposed Approach 6 3.1 Image Re-ranking Based on Quality 6 Chapter 4 Features 8 4.1 Basic Photography 10 4.1.1 Blurriness 10 4.1.2 Contrast 11 4.1.3 Noise 12 4.2 Color Distribution 13 4.2.1 Color Harmonization 13 4.2.2 Hue, Saturation and Value 14 4.2.3 Color Size 15 4.2.4 Standard Deviation of Hue 16 4.2.5 Hue Count 16 4.2.6 Largest Area 17 4.3 Composition 18 4.3.1 Region of Interest 18 4.3.2 Horizontal Balance 19 4.3.3 Edge Distributions 20 4.4 Image Retrieval Features 21 4.4.1 CBIR Image Similarity Score 21 4.4.2 Basic, Composition and Tone feature vector 22 Chapter 5 Experiments 23 5.1 Datasets 23 5.1.1 Photo.net 23 5.1.2 DPChallenge 24 5.1.3 INRIA Holidays dataset 25 5.1.4 Google & Flickr 25 5.2 Evaluation Metrics 26 5.2.1 NDCG 26 5.2.2 Kendall’s tau 26 5.2.3 ERR 27 5.3 Overall Performance 27 5.4 Feature Analysis 29 5.4.1 F-score 29 5.4.2 Analysis of different image types 31 5.4.3 Importance of retrieval features 32 5.5 Compare with original rankings from search engine 34 5.6 Robustness 36 5.7 Training Size & Parameter n of Retrieval Features 37 5.8 User Study 39 Chapter 6 Conclusion and Future Work 42 6.1 Conclusion 42 6.2 Future Work 42 REFERENCE 43 Publications 46 | |
dc.language.iso | en | |
dc.title | 基於美學品質重新排列網路圖片 | zh_TW |
dc.title | Re-ranking Web Images based on Aesthetic Quality | en |
dc.type | Thesis | |
dc.date.schoolyear | 98-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 曾新穆,陳信希,彭文志,徐宏民 | |
dc.subject.keyword | 圖片搜尋,圖片品質,圖片排序, | zh_TW |
dc.subject.keyword | Image Search,Image Quality,Image Ranking, | en |
dc.relation.page | 46 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2010-08-11 | |
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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