本篇論文使用一種記錄輪廓特質的方法，來當圖形的形狀描述子。這個方法是將輪廓用 n 個不連續的點表示，對於每一個參考點，記錄剩下n – 1個點和參考點的相對位置。可以由研究發現，當輪廓被旋轉時，這樣的記錄結果也會被旋轉，因此，如果這些結果彼此有旋轉關係的話，就把他群聚在一起，並且用一個來符號表示。所以，本來輪廓是由 n 個點來表示的，現在變成用n個符號來表示。對於每個輪廓，統計這些符號出現的次數後再與資料庫中的圖片做比對，就可以快速地找到輪廓相似，或是輪廓經由旋轉過後相似的圖。
本篇論文把這種輪廓比對的技巧，套用在3D影像擷取系統中，這個系統是將3D圖片轉換成2D圖片，以各種不同角度的2D圖片來呈現3D圖片的場景，是一個符合人類思考模式的系統。如此一來，對於3D影像擷取的結果將會有很高的準確性。

In this work we use shape context as our shape descriptor. The representation for a shape is a discrete set of n points. For each of these points, the shape context is a histogram of the relative positions of the remaining points. When a shape is rotated, the shape context is rotated too. We group the rotated shape contexts together and then label each group by an integer. Therefore, a shape is represented by a set of label. Using the histogram of label frequencies can quickly and efficiently search for similar or rotational shapes.
We use this shape retrieval method to integrate with an 3D existent retrieval system. This system transforms the 3D pictures to the 2D pictures, using each kind of different angle's 2D pictures to present scenes of 3D pictures. The system will learn the user’s semantic subjectivity. Hence, well accuracy is demonstrated in the results of image retrieval.

[1] A. Ghosh and N. Petkov, “Robuatness of Shape Descriptors to Incomplete Contour Representations,” IEEE trans. Pattern Analysis and Machine Intelligence, vol. 27, pp. 1793－1804, 2005.

[2] A. Thayananthan, B. Stenger, “Shape Context and Chamfer Matching in Cluttered Scenes,” Proc. IEEE Comp. Soc. Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 127－133, 2003.

[3] B. Leibe and B. Schiele, “Analyzing Appearance and Contour Based Methods for Object Categorization,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 409 – 415, 2003.

[4] C. Faloutsos, R. Barber, M. Flickner, J. Hafner, W. Niblack, D. Petkovic and W. Equitz, “Efficient and Effective Querying by Image Content,” Journal of Intelligent Systems, pp. 231－262, 1994.

[5] C. T. Hsu, “Research of Multimedia Processing Laboratory,” (http://mp.cs.nthu.edu.tw/research.htm)

[6] D. Liu and T. Chen, “Soft Shape Context for Iterative Closest Point Registration,” International Conf. Image Processing, vol. 2, pp. 24－27, 2004.

[7] F. Jing, M. Li, H. J. Zhang, and B. Zhang, “Relevance Feedback in Region-Based Image Retrieval,” IEEE Trans. Circuits and Systems for Video Technology, vol. 14, pp. 672 – 681, 2004.

[8] F. Jing, M. Li, H. J. Zhang, and B. Zhang, “An Efficient and Effective Region-Based Image Retrieval Framework,” IEEE Trans. Image Processing, vol. 13, pp. 699 – 709, 2004.

[9] F. Mokhtarian and A. K. Mackworth, “A Theory of Multiscale, Curvature-Based Shape Representation for Planar Curves,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 14, pp. 789－805, 1992.

[10] F. Jing, M. Li, H. J. Zhang, and B. Zhang, “Unsupervised Image Segmentation Using Local Homogeneity Analysis,” Proc. IEEE Int. Symp. Circuits and Systems, vol. 2, pp. 456 – 459, 2003.

[11] F. Mokhtarian and R. Suomela, “Robust Image Corner Detection Through Curvature Scale Space,” IEEE trans. Pattern Analysis and Machine Intelligence, vol. 20, pp. 1376－1381, 1998.

[12] G. Mori, S. Belongie and J. Malik, “Shape Contexts Enable Efficient Retrieval of Similar Shapes,” Proc. IEEE Comp. Soc. Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 723－730, 2001.

[13] G. Mori, S. Belongie and J. Malik, “Efficient Shape Matching Using Shape Contexts,” IEEE trans. Pattern Analysis and Machine Intelligence, vol. 27, pp. 1832－1837, 2005.

[14] G. Carneiro and A. D. Jepson, “Pruning Local Feature Correspondences Using Shape Context,” Proc. Int. Conf. Pattern Recognition, vol. 3, pp. 16－19, 2004.

[15] H. Zhang and J. Malik, “Learning A Discriminative Classifier Using Shape Context Distances,” Proc. IEEE Comp. Soc. Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 242－247, 2003.

[16] J. Huang, S. R. Kumar, M. Mitra, W.-J. Zhu, and R. Zabih, “Image Indexing Using Color Correlograms,”Proc. IEEE Comp. Soc. Conf. Computer Vision and Pattern Recognition, pp. 762－768, 1997.

[17] J. Z. Wang, J. Li, and G. Wiederhold, ”SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture Libraries,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, pp. 947 – 963, 2001.

[18] J. Li, J. Z. Wang, and G. Wiederhold, “IRM: Integrated Region Matching for Image Retrieval,” Proc. of the 8th ACM Int. Conf. on Multimedia, pp. 147 – 156, 2000.

[19] K. Vu, K. A. Hua, and W. Tavanapong, “Image Retrieval Based on Regions of Interest,” IEEE Trans. Knowledge and Data Engineering, vol. 15, pp. 1045 – 1049, 2003.

[20] M. Stricker and M. Orengo, “Similarity of Color Images,” Proc. SPIE Storage and Retrieval for Image and Video Databases III, vol. 2420, pp. 381 – 392, 1995..

[21] M. Yusuf and T. Haider, “Recognition of Handwritten Urdu Digits Using Shape Context,” Proc. Int. Multitopic Conference, pp. 569－572, 2004.

[22] R. Baeza-Yates and B. Ribeiro-Neto, Modern Information Retrieval. Reading, MA: Addison-Wesley, 1999

[23] S. Belongie and J. Malik, “Matching with Shape Contexts,” IEEE proc. Content-Based Access of Image and Video Libraries, pp. 20－26, 2000.

[24] S. Belongie, J. Malik and J. Puzicha, “Shape Matching and Object Recognition Using Shape Contexts,” IEEE trans. Pattern Analysis and Machine Intelligence, vol. 24, pp. 509－522, 2002.

[25] “The Math Works,” (http://www.mathworks.com/)

[26] W. C. Yen, “Image Retrieval by Using Shape Context,” Department of E.E. NCKU, Tainan, Taiwan, R.O.C., 2006.

[27] X. J. Qiu, Z. Q. Wang, S. H. Xia and J. T. Li, “Estimating Articulated Himan Pose from Video Using Shape Context,” Proc. IEEE Int. Symp. Signal Processing and Information Technology, pp. 583－588, 2005.

[28] Y. Deng, B. S. Manjunath, C. Kenney, M.S. Moore and H. Shin, “An Efficient Color Representation for Image Retrieval,” IEEE Trans. Image Processing, vol. 10, pp. 140－147, 2001.

[29] Ding-Yun Chen, Xiao-Pei Tian, Yu-Te Shen and Ming Ouhyoung, “Shape Distributions,” Computer Graphics Forum (EUROGRAPHICS'03), Vol. 22, No. 3, pp. 223－232, Sept. 2003.

[30] Johan W.H. Tangelder, Remco C. Veltkamp, “A survey of content based 3D shape retrieval methods,” Shape Modeling Applications, Proceedings 2004 pp. 145－156, 2004.

[31] Yi Liu, Jiantao Pu, Guyu Xin, Hongbin Zha, Weibin Liu, Yusuke Uehara, “A Robust Method for Shape-based 3D Model Retrieval,” Computer Graphics and Applications, 2004. PG 2004. Proceedings. 12th Pacific Conference on 6-8 Oct. 2004, pp. 3－9.

[32] Dong Xu, Hua Li, Zongkai Lin, “Content-based 3-D Shape Retrieval for Pervasive Computing,” Pervasive Computing and Applications, 2006 1st International Symposium on 3-5 Aug. 2006, pp. 206－211.

[33] 3DCAFE, http://www.3dcafe.com.

[34] Chia-Hui Lin, “Region-Based Image Retrieval Using Shape Context,” Department of E.E. NCKU, Tainan, Taiwan, R.O.C., 2007.