Abstract
We develop a probabilistic framework that computes 3D shape descriptors in a more rigorous and accurate manner than usual histogram-based methods for the purpose of 3D object retrieval. We first use a numerical analytical approach to extract the shape information from each mesh triangle in a better way than the sparse sampling approach. These measurements are then combined to build a probability density descriptor via kernel density estimation techniques, with a rule-based bandwidth assignment. Finally, we explore descriptor fusion schemes. Our analytical approach reveals the true potential of density-based descriptors, one of its representatives reaching the top ranking position among competing methods.
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Tangelder, J.W.H., Veltkamp, R.C.: A survey of content based 3D shape retrieval methods. In: Proc. of the Shape Modeling International 2004 (SMI 2004), Genoa, Italy, pp. 145–156 (2004)
Shilane, P., Min, P., Kazhdan, M., Funkhouser, T.: The Princeton shape benchmark. In: Proc. of the Shape Modeling International 2004 (SMI 2004), Genoa, Italy, pp. 167–178 (2004)
Härdle, W., Müller, M., Sperlich, S., Werwatz, A.: Nonparametric and Semiparametric Models. Springer Series in Statistics. Springer, Heidelberg (2004)
Paquet, E., Rioux, M.: Nefertiti: a query by content software for three-dimensional models databases management. In: Proc. of the International Conference on Recent Advances in 3-D Digital Imaging and Modeling (NRC 1997), p. 345. IEEE Computer Society Press, Los Alamitos (1997)
Osada, R., Funkhouser, T., Chazelle, B., Dobkin, D.: Shape distributions. ACM Trans. Graph. 21, 807–832 (2002)
Horn, B.K.P.: Extended Gaussian images. Proc. of the IEEE 72, 1671–1686 (1984)
Kang, S.B., Ikeuchi, K.: The complex EGI: A new representation for 3D pose determination. IEEE Trans. Pattern Anal. and Mach. Intell. 15, 707–721 (1993)
Zaharia, T., Prêteux, F.: Indexation de maillages 3D par descripteurs de forme. In: Actes 13ème Congrès Francophone AFRIF-AFIA Reconnaissance des Formes et Intelligence Artificielle (RFIA 2002), Angers, France, pp. 48–57 (2002)
Akgül, C.B., Sankur, B., Yemez, Y., Schmitt, F.: A framework for histogram-induced 3D descriptors. In: European Signal Processing Conference (EUSIPCO 2006), Florence, Italy (2006)
Press, W.H., Flannery, B.P., Teukolsky, S.A.: Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, Cambridge (1992)
Yang, C., Duraiswami, R., Gumerov, N.A., Davis, L.: Improved fast Gauss transform and efficient kernel density estimation. In: ICCV, vol. 1, p. 464 (2003)
Vranić, D.V.: 3D Model Retrieval. PhD thesis, University of Leipzig (2004)
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Akgül, C.B., Sankur, B., Schmitt, F., Yemez, Y. (2006). Density-Based Shape Descriptors for 3D Object Retrieval. In: Gunsel, B., Jain, A.K., Tekalp, A.M., Sankur, B. (eds) Multimedia Content Representation, Classification and Security. MRCS 2006. Lecture Notes in Computer Science, vol 4105. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11848035_43
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DOI: https://doi.org/10.1007/11848035_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-39392-4
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