Similar articles

Abstract: In this paper, we present a novel Support Vector Machine active learning algorithm for effective 3D model retrieval using the concept of relevance feedback. The proposed method learns from the most informative objects which are marked by the user, and then creates a boundary separating the relevant models from irrelevant ones. What it needs is only a small number of 3D models labelled by the user. It can grasp the user’s semantic knowledge rapidly and accurately. Experimental results showed that the proposed algorithm significantly improves the retrieval effectiveness. Compared with four state-of-the-art query refinement schemes for 3D model retrieval, it provides superior retrieval performance after no more than two rounds of relevance feedback.

[1] Ansary, T.F., Daoudi, M., Vandeborre, J.P., 2007. A Bayesian 3-D search engine using adaptive views clustering. IEEE Trans. on Multimedia, 9(1):78-88.

[2] Atmosukarto, I., Leow, W.K., Huang, Z.Y., 2005. Feature Combination and Relevance Feedback for 3D Model Retrieval. Proc. 11th Int. Multimedia Modeling Conf. Melbourne, Australia, p.334-339.

[3] Bang, H.Y., Chen, T., 2002. Feature Space Warping: An Approach to Relevance Feedback. IEEE Int. Conf. on Image Processing. Rochester, New York, USA, p.22-25.

[4] Burges, C.J.C., 1998. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2):121-167.

[5] Bustos, B., Keim, D.A., Saupe, D., Schreck, T., Vranic, D.V., 2005. Feature-based similarity search in 3D object databases. ACM Computing Surveys, 37(4):345-387.

[6] Chen, D.Y., Tian, X.P., Shen, Y.T., Ouhyoung, M., 2003. On visual similarity based 3D model retrieval. Computer Graphics Forum, 22(3):223-232.

[7] Daras, P., Zarpalas, D., Tzovaras, D., Strintzis, M.G., 2006. Efficient 3D model search and retrieval using generalized 3D Radon transforms. IEEE Trans. on Multimedia, 8(1):101-114.

[8] Elad, M., Tal, A., Ar, S., 2001. Content Based Retrieval of VRML Objects—An Iterative and Interactive Approach. Proc. Eurographics Workshop on Multimedia. Manchester, UK, p.97-108.

[9] Funkhouser, T., Min, P., Kazhdan, M., Chen, J., 2003. A search engine for 3D models. ACM Trans. on Graphics, 22(1):83-105.

[10] Funkhouser, T., Kazhdan, M., Min, P., Shilane, P., 2005. Shape-based retrieval and analysis of 3D models. Commun. ACM, 48(6):58-64.

[11] He, X.F., King, O., Ma, W.Y., 2003. Learning a semantic space from user’s relevance feedback for image retrieval. IEEE Trans. on Circuits and Systems for Video Technology, 13(1):39-48.

[12] Iyer, N., Jayanti, S., Lou, K.Y., Kalyanaraman, Y., Ramani, K., 2005. Three-dimensional shape searching: state-of-the-art review and future trends. Computer-Aided Design, 37(5):509-530.

[13] Kazhdan, M., Funkhouser, T., Rusinkiewicz, S., 2004. Shape matching and anisotropy. ACM Trans. on Graphics, 23(3):623-629.

[14] Kolonias, I., Tzovaras, D., Malassiotis, S., Strintzis, M.G., 2005. Fast content-based search of VRML models based on shape descriptors. IEEE Trans. on Multimedia, 7(1):114-126.

[15] Leifman, G., Meir, R., Tal, A., 2005. Semantic-oriented 3D shape retrieval using relevance feedback. The Visual Computer, 21(8):865-875.

[16] Mandel, M.I., Poliner, G.E., Ellis, D.P., 2006. Support vector machine active learning for music retrieval. Multimedia Systems, 12(1):3-13.

[17] Novotni, M., Park, G.J., Wessel, R., Klein, R., 2005. Evaluation of Kernel Based Methods for Relevance Feedback in 3D Shape Retrieval. Proc. 4th Int. Workshop on Content-based Multimedia Indexing. Riga, Latvia.

[18] Osada, R., Funkhouser, T., Saupe, D., Chazelle, B., Dobkin, D., 2002. Shape distributions. ACM Trans. on Graphics, 21(4):807-832.

[19] Paquet, E., Rioux, M., 1999. Nefertiti: a query by content software for three-dimensional models databases management. Image and Vision Computing, 17(2):157-166.

[20] Pu, J.T., Ramani, K., 2006. On visual similarity based 2D drawing retrieval. Computer-Aided Design, 38(3):249-259.

[21] Regli, W.C., Cicirello, V.A., 2000. Managing digital libraries for computer-aided design. Computer-Aided Design, 32(2):119-132.

[22] Sebastiani, F., 2002. Machine learning in automated text categorization. ACM Computing Surveys, 34(1):1-47.

[23] Shilane, P., Min, P., Kazhdan, M., Funkhouser, T., 2004. The Princeton Shape Benchmark. Proc. Shape Modeling and Applications. Palazzo Ducale, Italy, p.167-178.

[24] Tong, S., Chang, E., 2001. Support Vector Machine Active Learning for Image Retrieval. Proc. 9th Annual ACM Int. Conf. on Multimedia. Ottawa, Ontario, Canada, p.107-118.

[25] Vranic, D.V., Saupe, D., 2002. Description of 3D-shape Using a Complex Function on the Sphere. Proc. IEEE Int. Conf. on Multimedia and Expo. Lausanne, Switzerland, p.177-180.

[26] Vranic, D.V., 2004. 3D Model Retrieval. Ph.D Thesis, University of Leipzig, German.

[27] Vranic, D.V., 2005. DESIRE: A Composite 3D-shape Descriptor. Proc. IEEE Int. Conf. on Multimedia and Expo. Amsterdam, the Netherland, p.962-965.

[28] Yeh, J.S., Chen, D.Y., Chen, B.Y., Ouhyoung, M., 2005. A web-based three-dimensional protein retrieval system by matching visual similarity. Bioinformatics, 21(13):3056-3057.

[29] Zhou, X.S., Huang, T.S., 2002. Unifying keywords and visual contents in image retrieval. IEEE Multimedia, 9(2):23-33.

[30] Zhou, X.S., Thomas, S.H., 2003. Relevance feedback in image retrieval: a comprehensive review. Multimedia Systems, 8(6):536-544.