Abstract
Distance measures, along with shape features, are the most critical components in a shape-based 3D model retrieval system. Given a shape feature, an optimal distance measure will vary per query, per user, or per database. No single, fixed distance measure would be satisfactory all the time. This paper focuses on a method to adapt distance measure to the database to be queried by using learning-based dimension reduction algorithms. We experimentally compare six such dimension reduction algorithms, both linear and non-linear, for their efficacy in the context of shape-based 3D model retrieval. We tested the efficacy of these methods by applying them to five global shape features. Among the dimension reduction methods we tested, non-linear manifold learning algorithms performed better than the other, e.g. linear algorithms such as principal component analysis. Performance of the best performing combination is roughly the same as the top finisher in the SHREC 2006 contest.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Atmosukarto, I., Leow, W.K., Huang, Z.: Feature Combination and Relevance Feedback for 3D Model Retrieval. In: Proc. MMM 2005, pp. 334–339 (2005)
Baeza-Yates, R., Ribiero-Neto, B.: Modern information retrieval. Addison-Wesley, Reading (1999)
Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15, 1373–1396 (2003)
Bratley, P., Fox, B.L., Niederreiter, H.: Algorithm 738: Programs to Generate Niederreiter’s Low-discrepancy Sequences. ACM TOMS Algorithm 738
Bustos, B., Keim, D., Saupe, D., Schreck, T., Vranić, D.: Automatic Selection and Combination of Descriptors for Effective 3D Similarity Search. In: Proc. IEEE MCBAR 2004, pp. 514–521 (2004)
Chen, S., Cowan, C.F.N., Grant, P.M.: Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks. IEEE Trans. on Neural Networks 2(2), 302–309 (1991)
Chen, D.-Y., Tian, X.-P., Shen, Y.-T., Ouhyoung, M.: On Visual Similarity Based 3D Model Retrieval. Computer Graphics Forum 22(3), 223–232 (2003)
Elad, M., Tal, A., Ar, S.: Content based retrieval of vrml objects – an iterative and interactive approach. In: Proc. EG Multimedia, vol. 39, pp. 97–108 (2001)
Funkhouser, T., Min, P., Kazhdan, M., Chen, J., Halderman, A., Dobkin, D., Jacobs, D.: A Search Engine for 3D Models. ACM TOG 22(1), 83–105 (2003)
Haykin, S.: Neural network a comprehensive foundation, 2nd edn. Prentice-Hall, Englewood Cliffs (1999)
He, X., Niyogi, P.: Locality Preserving Projections. In: Advances in Neural Information Processing Systems, Vancouver, Canada, vol. 16 (2003), http://people.cs.uchicago.edu/~xiaofei/LPP.html
He, X., Ma, W.-Y., Zhang, H.-J.: Learning an Image Manifold for Retrieval. In: Proc. ACM Multimedia 2004, pp. 17–23 (2004)
Iyer, M., Jayanti, S., Lou, K., Kalyanaraman, Y., Ramani, K.: Three Dimensional Shape Searching: State-of-the-art Review and Future Trends. Computer Aided Design 5(15), 509–530 (2005)
Kazhdan, M., Funkhouser, T., Rusinkiewicz, S.: Rotation Invariant Spherical Harmonics Representation of 3D Shape Descriptors. In: Proc. Symposium of Geometry Processing 2003, pp. 167–175 (2003), http://www.cs.jhu.edu/~misha/
Leifman, G., Meir, R., Tal, A.: Semantic-oriented 3d shape retrieval using relevance feedback. The Visual Computer (Pacific Graphics) 21(8-10), 865–875 (2005)
Novotni, M., Park, G.-J., Wessel, R., Klein R.: Evaluation of Kernel Based Methods for Relevance Feedback in 3D Shape Retrieval. In: Proc. The Fourth International Workshop on Content-Based Multimedia Indexing (CBMI 2005) (2005)
NTU 3D Model Database ver.1, http://3d.csie.ntu.edu.tw/
Ohbuchi, R., Takei, T.: Shape-Similarity Comparison of 3D Shapes Using Alpha Shapes. In: Proc. Pacific Graphics 2003, pp. 293–302 (2003)
Ohbuchi, R., Minamitani, T., Takei, T.: Shape-similarity search of 3D models by using enhanced shape functions. IJCAT 23(3/4/5), 70–85 (2005)
Ohbuchi, R., Kobayashi, J.: Unsupervised Learning from a Corpus for Shape-Based 3D Model Retrieval. In: Proc. ACM MIR 2006, pp. 163–172 (2006)
Roweis, S.T., Saul, L.K.: Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science 290(5500), 2323–2326 (2000)
Shilane, P., Min, P., Kazhdan, M., Funkhouser, T.: The Princeton Shape Benchmark. In: Proc. SMI 2004, pp. 167–178 (2004), http://shape.cs.princeton.edu/search.html
Statistical Pattern Recognition Toolbox for Matlab, http://cmp.felk.cvut.cz/cmp/software/stprtool/index.html
Tangelder, J., Veltkamp, R.C.: A Survey of Content Based 3D Shape Retrieval Methods. In: Proc. SMI 2004, pp. 145–156 (2004)
Tanenbaum, J.B., de Silva, V., Langford, J.C.: A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science 290(5500), 2319–2323 (2000)
Veltkamp, R.C., Ruijsenaars, R., Spagnuolo, M., Van Zwol, R., ter Haar, F.: SHREC, 3D Shape Retrieval Contest, Utrecht University Dept. Information and Computing Sciences. Technical Report UU-CS-2006-030 (2006), http://give-lab.cs.uu.nl/shrec/shrec2006/index.html , , ISSN: 0924-3275
Vranić, D.V.: 3D Model Retrieval, Ph.D. Thesis, University of Leipzig (2004), http://merkur01.inf.uni-konstanz.de/CCCC/
Wahl, E., Hillenbrand, U., Hirzinger, G.: Surflet-Pair-Relation Histograms: A Statistical 3D-Shape Representation for Rapid Classification. In: Proc. 3DIM 2003, pp. 474–481 (2003)
Zhang, Z., Zha, H.: Principal manifolds and nonlinear dimension reduction via local tangent space alignment. SIAM Journal of Scientific Computing 26(1), 313–338 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ohbuchi, R., Kobayashi, J., Yamamoto, A., Shimizu, T. (2008). Comparison of Dimension Reduction Methods for Database-Adaptive 3D Model Retrieval. In: Boujemaa, N., Detyniecki, M., Nürnberger, A. (eds) Adaptive Multimedia Retrieval: Retrieval, User, and Semantics. AMR 2007. Lecture Notes in Computer Science, vol 4918. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79860-6_16
Download citation
DOI: https://doi.org/10.1007/978-3-540-79860-6_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-79859-0
Online ISBN: 978-3-540-79860-6
eBook Packages: Computer ScienceComputer Science (R0)