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題名 藉由直覺性素描與輔助影像的模型搜尋技術
Model Retrieval by Intuitive Sketching and Suggestive Reference
作者 李亞憲
Lee, Ya Hsien
貢獻者 紀明德
Chi, Ming Te
李亞憲
Lee, Ya Hsien
關鍵詞 模型搜尋
筆觸圖
直覺性繪畫
輔助影像
model retrieval
sketch
intuitive sketching
suggestive reference
日期 2016
上傳時間 1-Mar-2016 10:41:13 (UTC+8)
摘要 本篇論文建立一個藉由直覺性素描搜尋模型的系統，結合筆觸繪圖搜尋手繪圖與模型。希望可以藉由本系統，提供使用者比起關鍵字或模型搜尋模型，更加方便的模型搜尋工具。系統主要分為建立索引檔、比對特徵向量和使用者介面三個部分。建立索引檔部分要將三維模型處理成資料庫可認知的資料型態，首先將模型旋轉到不同角度並且將之從三維空間描繪成二維模型投影圖，再透過分類演算法把模型投影圖和手繪圖描述為二維特徵向量。比對特徵向量部分需建立手繪圖資料庫和三維模型資料庫的橋梁，藉由計算兩者的特徵向量之間的距離與角度，得到相似度的排序。使用者介面部分提供直覺性使用者繪畫的介面，以不影響使用者創造性的前提下，在使用者繪畫過程中給予最相似於使用者繪畫的手繪圖結果，使用者可以藉由臨摹此結果更貼近所想繪畫的物體，更進一步地取得模型的搜尋結果。最後我們將透過統計方法去驗證系統的有效性。
We proposed an intuitive model retrieval system with a sketch interface for a database contains sketch drawings and 3D models. Benefit the sketch interface, the proposed system can facilitate the search process better than keyword query or search by 3D model. The system begins with offline indexing preprocess which convert the 3D models into feature vectors. Under best view selection, we render each 3D model into a 2D feature line image. Then classification method will apply the line images and sketching images in model database to build the feature vector. The rank of matching is computed with the angle between the feature vector of input sketch image and feature line images in the database. To extend the usability, we design a sketch interface for searching the best match result during the drawing process. For suggesting the drawing hint, candidate matching results are listed aside to the sketch input screen. We use statistical method to evaluate the feasibility of the proposed system.
參考文獻 [1] A. Krizhevsky, I. Sutskever, and G. E. Hinton. ImageNet classification with deep convolutional neural networks. In NIPS, pages 1106–1114, 2012.
[2] Bay, H., Tuytelaars, T., And Gool, L. J. V. 2006. SURF: Speeded up robust features. In ECCV, 404–417.
[3] Canny, J. 1986. A computational approach to edge detection.
IEEE TPAMI 8, 6, 679–698.
[4] Cao, Y., Wang, H., Wang, C., Li, Z., Zhang, L., and Zhang, L. 2010. Mindfinder: Finding images by sketching. In ACM Multimedia International Conference.
[5] Chen, D.-Y., Tian, X.-P., Shen, Y.-T., and Ouhyoung, M. 2003. On visual similarity based 3d model retrieval. Comput. Graph. Forum (Proc. Eurographics) 22, 3, 223–232.
[6] Chatfield, K., Simonyan, K., Vedaldi, A., and Zisserman, A. Return of the devil in the details: Delving deep into convolutional nets. In Proc. BMVC., 2014.
[7] Decarlo, D., Finkelstein, A., Rusinkiewicz, S., and Santella, A. 2003. Suggestive contours for conveying shape. ACM TOG (Proc. SIGGRAPH) 22, 3, 848–855.
[8] Dixon, D., Prasad, M., and Hammond, T. 2010. icandraw: Using sketch recognition and corrective feedback to assist a user in drawing human faces. ACM CHI.
[9] Eitz, M., Richter, R., Boubekeur, T., Hildebrand, K., and Alexa, M. 2012. Sketch-based shape retrieval. ACM Transactions on Graphics 31, 4, 31:1–31:10.
[10] Funkhouser, T., Min, P., Kazhdan, M., Chen, J., Halderman, A., Dobkin, D., and Jacobs, D. 2003. A search engine for 3D models. ACM TOG 22, 1, 83–105.
[11] F. Wang, L. Kang, and Y. Li. Sketch-based 3d shape retrieval using convolutional neural networks. In arXiv preprint arXiv:1504.03504, 2015.
[12] H. Su, S. Maji, E. Kalogerakis, and E. G. Learned-Miller. Multi-view convolutional neural networks for 3D shape recognition. In ICCV, 2015.
[13] M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional networks. CoRR, abs/1311.2901, 2013.
[14] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. Imagenet: A large-scale hierarchical image database. In Proc. CVPR, 2009.
[15] J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell. Decaf: A deep convolutional activation feature for generic visual recognition. CoRR, abs/1310.1531, 2013.
[16] Jun-Yan Zhu, Yong Jae Lee, Alexei A. Efros.2014 AverageExplorer: interactive exploration and alignment of visual data collections. ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH 2014 TOG Volume 33 Issue 4, July 2014
[17] Lee, Y., Zitnick, C., and Cohen, M. 2011. ShadowDraw: real-time user guidance for freehand drawing. ACM TOG (Proc. SIGGRAPH) 30, 4, 27:1–27:10.
[18] Li B., Lu Y., Godil A., Schreck T., Aono M., Johan H., Saavedra J. M., Tashiro S.: SHREC’13 track: Large scale sketch-based 3D shape
retrieval. In 3DOR (2013), pp. 1–9.
[19] L¨O Ffler, J. 2000. Content-based retrieval of 3D models in distributed web databases by visual shape information. In Int’l. Conf. Information Visualization, 82–87.
[20] Lowe, D. 2004. Distinctive image features from scale-invariant keypoints. IJCV 60, 2, 91–110.
[21] Potcharapol Suteparuk, Emmanuel Tsukerman .Geometric Modeling and Processing Project: Mesh Simplication and Expressive Rendering.2013
[22] Shilane, P., Min, P., Kazhdan, M., and Funkhouser, T. 2004. The Princeton Shape Benchmark. In Proc. Shape Modeling International, 167–178.
[23] Siddhartha Chaudhuri , Vladlen Koltun, Data-driven suggestions for creativity support in 3D modeling, ACM SIGGRAPH Asia 2010 papers, December 15-18, 2010, Seoul, South Korea
[24] SIVIC, J., AND ZISSERMAN, A. 2003. Video Google: a text retrieval approach to object matching in videos. In ICCV, 1470– 1477.
[25] Squire, D., Mueller, W., Mueller, H., and Raki, J. 1999. Content-based query of image databases. In Scand. Conf. on Image Analysis, 143–149..
[26] Sykora ´ , D., Kavan, L., Cˇ Ad´Ik, M., Jamriska ˇ , O., Jacobson, A., Whited, B., Simmons, M., and Sorkinehornung, O. 2014. Ink-and-ray: Bas-relief meshes for adding global illumination effects to hand-drawn characters. ACM Trans. Graphics 33.
描述 碩士
國立政治大學
資訊科學學系
102753036
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0102753036
資料類型 thesis
dc.contributor.advisor 紀明德zh_TW
dc.contributor.advisor Chi, Ming Teen_US
dc.contributor.author (Authors) 李亞憲zh_TW
dc.contributor.author (Authors) Lee, Ya Hsienen_US
dc.creator (作者) 李亞憲zh_TW
dc.creator (作者) Lee, Ya Hsienen_US
dc.date (日期) 2016en_US
dc.date.accessioned 1-Mar-2016 10:41:13 (UTC+8)-
dc.date.available 1-Mar-2016 10:41:13 (UTC+8)-
dc.date.issued (上傳時間) 1-Mar-2016 10:41:13 (UTC+8)-
dc.identifier (Other Identifiers) G0102753036en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/81529-
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) 102753036zh_TW
dc.description.abstract (摘要) 本篇論文建立一個藉由直覺性素描搜尋模型的系統，結合筆觸繪圖搜尋手繪圖與模型。希望可以藉由本系統，提供使用者比起關鍵字或模型搜尋模型，更加方便的模型搜尋工具。系統主要分為建立索引檔、比對特徵向量和使用者介面三個部分。建立索引檔部分要將三維模型處理成資料庫可認知的資料型態，首先將模型旋轉到不同角度並且將之從三維空間描繪成二維模型投影圖，再透過分類演算法把模型投影圖和手繪圖描述為二維特徵向量。比對特徵向量部分需建立手繪圖資料庫和三維模型資料庫的橋梁，藉由計算兩者的特徵向量之間的距離與角度，得到相似度的排序。使用者介面部分提供直覺性使用者繪畫的介面，以不影響使用者創造性的前提下，在使用者繪畫過程中給予最相似於使用者繪畫的手繪圖結果，使用者可以藉由臨摹此結果更貼近所想繪畫的物體，更進一步地取得模型的搜尋結果。最後我們將透過統計方法去驗證系統的有效性。zh_TW
dc.description.abstract (摘要) We proposed an intuitive model retrieval system with a sketch interface for a database contains sketch drawings and 3D models. Benefit the sketch interface, the proposed system can facilitate the search process better than keyword query or search by 3D model. The system begins with offline indexing preprocess which convert the 3D models into feature vectors. Under best view selection, we render each 3D model into a 2D feature line image. Then classification method will apply the line images and sketching images in model database to build the feature vector. The rank of matching is computed with the angle between the feature vector of input sketch image and feature line images in the database. To extend the usability, we design a sketch interface for searching the best match result during the drawing process. For suggesting the drawing hint, candidate matching results are listed aside to the sketch input screen. We use statistical method to evaluate the feasibility of the proposed system.en_US
dc.description.tableofcontents 摘要 ii
Abstract iii
目錄 iv
圖目錄 vi
第一章 緒論 1
1.1 研究動機與目的 1
1.2 問題描述 2
1.3 論文貢獻 3
1.4 論文章節架構 3
第二章 相關研究 4
2.1 圖片特徵儲存方式 4
2.2 以筆觸搜尋圖片 5
2.3 以筆觸搜尋模型 7
2.4 以卷積神經網路搜尋模型 9
第三章 研究方法與步驟 11
3.1 系統架構 11
3.2 模型的投影 13
3.3 建立資料庫 14
3.3.1 投影圖描繪 15
3.3.2 Bag-Of-Feature檢索 17
3.3.3 卷積神經網路 20
3.4 搜尋結果排序 22
3.4.1 相似度比對 22
3.4.2 結果排序 23
3.5 修改手繪資料庫 25
第四章 實驗結果與討論 32
4.1 實作與實驗環境 32
4.2 評估方法 32
4.3 實作與實驗結果 33
4.3.1 模型投影圖搜尋模型投影圖 34
4.3.2 手繪圖搜尋手繪圖 35
4.3.3 手繪圖搜尋模型投影圖 36
4.3.4 即時繪畫搜尋 37
第五章 實驗數據與比較 39
第六章 結論與未來發展 43
參考文獻 45		zh_TW
dc.format.extent 2546693 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0102753036en_US
dc.subject (關鍵詞) 模型搜尋zh_TW
dc.subject (關鍵詞) 筆觸圖zh_TW
dc.subject (關鍵詞) 直覺性繪畫zh_TW
dc.subject (關鍵詞) 輔助影像zh_TW
dc.subject (關鍵詞) model retrievalen_US
dc.subject (關鍵詞) sketchen_US
dc.subject (關鍵詞) intuitive sketchingen_US
dc.subject (關鍵詞) suggestive referenceen_US
dc.title (題名) 藉由直覺性素描與輔助影像的模型搜尋技術zh_TW
dc.title (題名) Model Retrieval by Intuitive Sketching and Suggestive Referenceen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) [1] A. Krizhevsky, I. Sutskever, and G. E. Hinton. ImageNet classification with deep convolutional neural networks. In NIPS, pages 1106–1114, 2012.
[2] Bay, H., Tuytelaars, T., And Gool, L. J. V. 2006. SURF: Speeded up robust features. In ECCV, 404–417.
[3] Canny, J. 1986. A computational approach to edge detection.
IEEE TPAMI 8, 6, 679–698.
[4] Cao, Y., Wang, H., Wang, C., Li, Z., Zhang, L., and Zhang, L. 2010. Mindfinder: Finding images by sketching. In ACM Multimedia International Conference.
[5] Chen, D.-Y., Tian, X.-P., Shen, Y.-T., and Ouhyoung, M. 2003. On visual similarity based 3d model retrieval. Comput. Graph. Forum (Proc. Eurographics) 22, 3, 223–232.
[6] Chatfield, K., Simonyan, K., Vedaldi, A., and Zisserman, A. Return of the devil in the details: Delving deep into convolutional nets. In Proc. BMVC., 2014.
[7] Decarlo, D., Finkelstein, A., Rusinkiewicz, S., and Santella, A. 2003. Suggestive contours for conveying shape. ACM TOG (Proc. SIGGRAPH) 22, 3, 848–855.
[8] Dixon, D., Prasad, M., and Hammond, T. 2010. icandraw: Using sketch recognition and corrective feedback to assist a user in drawing human faces. ACM CHI.
[9] Eitz, M., Richter, R., Boubekeur, T., Hildebrand, K., and Alexa, M. 2012. Sketch-based shape retrieval. ACM Transactions on Graphics 31, 4, 31:1–31:10.
[10] Funkhouser, T., Min, P., Kazhdan, M., Chen, J., Halderman, A., Dobkin, D., and Jacobs, D. 2003. A search engine for 3D models. ACM TOG 22, 1, 83–105.
[11] F. Wang, L. Kang, and Y. Li. Sketch-based 3d shape retrieval using convolutional neural networks. In arXiv preprint arXiv:1504.03504, 2015.
[12] H. Su, S. Maji, E. Kalogerakis, and E. G. Learned-Miller. Multi-view convolutional neural networks for 3D shape recognition. In ICCV, 2015.
[13] M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional networks. CoRR, abs/1311.2901, 2013.
[14] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. Imagenet: A large-scale hierarchical image database. In Proc. CVPR, 2009.
[15] J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell. Decaf: A deep convolutional activation feature for generic visual recognition. CoRR, abs/1310.1531, 2013.
[16] Jun-Yan Zhu, Yong Jae Lee, Alexei A. Efros.2014 AverageExplorer: interactive exploration and alignment of visual data collections. ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH 2014 TOG Volume 33 Issue 4, July 2014
[17] Lee, Y., Zitnick, C., and Cohen, M. 2011. ShadowDraw: real-time user guidance for freehand drawing. ACM TOG (Proc. SIGGRAPH) 30, 4, 27:1–27:10.
[18] Li B., Lu Y., Godil A., Schreck T., Aono M., Johan H., Saavedra J. M., Tashiro S.: SHREC’13 track: Large scale sketch-based 3D shape
retrieval. In 3DOR (2013), pp. 1–9.
[19] L¨O Ffler, J. 2000. Content-based retrieval of 3D models in distributed web databases by visual shape information. In Int’l. Conf. Information Visualization, 82–87.
[20] Lowe, D. 2004. Distinctive image features from scale-invariant keypoints. IJCV 60, 2, 91–110.
[21] Potcharapol Suteparuk, Emmanuel Tsukerman .Geometric Modeling and Processing Project: Mesh Simplication and Expressive Rendering.2013
[22] Shilane, P., Min, P., Kazhdan, M., and Funkhouser, T. 2004. The Princeton Shape Benchmark. In Proc. Shape Modeling International, 167–178.
[23] Siddhartha Chaudhuri , Vladlen Koltun, Data-driven suggestions for creativity support in 3D modeling, ACM SIGGRAPH Asia 2010 papers, December 15-18, 2010, Seoul, South Korea
[24] SIVIC, J., AND ZISSERMAN, A. 2003. Video Google: a text retrieval approach to object matching in videos. In ICCV, 1470– 1477.
[25] Squire, D., Mueller, W., Mueller, H., and Raki, J. 1999. Content-based query of image databases. In Scand. Conf. on Image Analysis, 143–149..
[26] Sykora ´ , D., Kavan, L., Cˇ Ad´Ik, M., Jamriska ˇ , O., Jacobson, A., Whited, B., Simmons, M., and Sorkinehornung, O. 2014. Ink-and-ray: Bas-relief meshes for adding global illumination effects to hand-drawn characters. ACM Trans. Graphics 33.		zh_TW