Abstract
Because of recent advances in graphics hard- and software, both the production and use of 3D models are increasing at a rapid pace. As a result, a large number of 3D models have become available on the web, and new research is being done on 3D model retrieval methods. Query and retrieval can be done solely based on associated text, as in image retrieval, for example (e.g. Google Image Search [1] and [2,3]). Other research focuses on shape-based retrieval, based on methods that measure shape similarity between 3D models (e.g., [4]). The goal of our work is to take current text- and shape-based matching methods, see which ones perform best, and compare those. We compared four text matching methods and four shape matching methods, by running classification tests using a large database of 3D models downloaded from the web [5]. In addition, we investigated several methods to combine the results of text and shape matching. We found that shape matching outperforms text matching in all our experiments. The main reason is that publishers of online 3D models simply do not provide enough descriptive text of sufficient quality: 3D models generally appear in lists on web pages, annotated only with cryptic filenames or thumbnail images. Combining the results of text and shape matching further improved performance. The results of this paper provide added incentive to continue research in shape-based retrieval methods for 3D models, as well as retrieval based on other attributes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Google: Image search, http://www.google.com/images
Sable, C.: Robust Statistical Techniques for the Categorization of Images Using Associated Text. PhD thesis, Columbia University (2003)
Smith, J.R., Chang, S.F.: Searching for images and videos on the world-wide web. Technical Report 459-96-25, Columbia University (1996)
Funkhouser, T., Min, P., Kazhdan, M., Chen, J., Halderman, A., Dobkin, D., Jacobs, D.: A search engine for 3D models. ACM Transactions on Graphics 22 (2003)
Shilane, P., Kazhdan, M., Min, P., Funkhouser, T.: The Princeton Shape Benchmark. In: Proc. Shape Modeling International, Genoa, Italy (2004)
MeshNose: 3D objects search engine, http://www.deepfx.com/meshnose
Suzuki, M.T.: A web-based retrieval system for 3D polygonal models. In: Proc. IFSA/NAFIPS, Vancouver, Canada, pp. 2271–2276 (2001)
Chen, D.Y., Ouhyoung, M.: A 3D object retrieval system based on multi-resolution Reeb graph. In: Proc. Computer Graphics Workshop, Taiwan, pp. 16–20 (2002)
Min, P.: A 3D Model Search Engine. PhD thesis, Princeton University (2004)
Miller, G.A.: WordNet: A lexical database for English. CACM 38, 39–41 (1995)
Min, P., Halderman, A., Kazhdan, M., Funkhouser, T.: Early experiences with a 3D model search engine. In: Proc. Web3D Symposium, St. Malo, France, pp. 7–18. ACM, New York (2003)
van Rijsbergen, C.J.: Information Retrieval. Butterworth, London (1979)
Salton, G.: Automatic text processing: the transformation, analysis, and retrieval of information by computer. Addison-Wesley, Reading (1988)
Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison-Wesley, Reading (1999)
Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys 34, 1–47 (2002)
FindSounds: Sound file search engine, http://www.findsounds.com
Sable, C.L., Hatzivassiloglou, V.: Text-based approaches for the categorization of images. In: Proc. Research and Advanced Technologies for Digital Libraries, Paris (1999)
Sable, C., McKeown, K., Church, K.W.: NLP found helpful (at least for one text categorization task). In: Proc. EMNLP, Philadelphia, PA (2002)
de Buenaga Rodríguez, M., Gómez-Hidalgo, J.M., Díaz-Agudo, B.: Using WordNet to complement training information in text categorization. In: Proc. RANLP, Stanford (1997)
Scott, S., Matwin, S.: Text classification using WordNet hypernyms. In: Proc. Workshop Usage of WordNet in Natural Language Processing Systems, pp. 45–52 (1998)
Benitez, A.B., Chang, S.F.: Semantic knowledge construction from annotated image collections. In: Proc. Int. Conf. on Multimedia and Expo ICME, Lausanne, Switzerland (2002)
Salton, G.: The SMART retrieval system. Prentice-Hall, Englewood Cliffs (1971)
Porter, M.: An algorithm for suffix stripping. Program 14, 130–137 (1980)
McCallum, A.: Bow: A toolkit for statistical language modeling, text retrieval, classification and clustering (1996), http://www.cs.cmu.edu/~mccallum/bow
Kullback, S., Leibler, R.: On information and sufficiency. Ann. Math. Stat. 22, 79–86 (1951)
Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)
Loncaric, S.: A survey of shape analysis techniques. Pattern Recognition 31 (1998)
Tangelder, J.W., Veltkamp, R.C.: A survey of content based 3D shape retrieval methods. In: Proc. Shape Modeling International, Genoa, Italy (2004)
Sundar, H., Silver, D., Gagvani, N., Dickinson, S.: Skeleton based shape matching and retrieval. In: Proc. SMI, Seoul, Korea (2003)
Hilaga, M., Shinagawa, Y., Kohmura, T., Kunii, T.L.: Topology matching for fully automatic similarity estimation of 3D shapes. In: Proc. SIGGRAPH, pp. 203–212 (2001)
Ashbrook, A.P., Thacker, N.A., Rockett, P.I., Brown, C.I.: Robust recognition of scaled shapes using pairwise geometric histograms. In: Proc. BMVC, Birmingham, UK, pp. 503–512 (1995)
Taubin, G., Cooper, D.: Object recognition based on moment (or algebraic) invariants. In: Geometric Invariance in Computer Vision, MIT Press, Cambridge (1992)
Kazhdan, M., Funkhouser, T., Rusinkiewicz, S.: Rotation invariant spherical harmonic representation of 3D shape descriptors. In: Proc. SGP, pp. 156–165. ACM, New York (2003)
Cyr, C.M., Kimia, B.B.: 3D object recognition using shape similarity-based aspect graph. In: Proc. ICCV, IEEE, Los Alamitos (2001)
Chen, D.Y., Ouhyoung, M., Tian, X.P., Shen, Y.T., Ouhyoung, M.: On visual similarity based 3D model retrieval. In: Proc. Eurographics, Granada, Spain (2003)
Vranic, D.V.: An improvement of rotation invariant 3D shape descriptor based on functions on concentric spheres. In: Proc. ICIP, vol. 3, pp. 757–760 (2003)
Jain, A.K., Duin, R.P., Mao, J.: Statistical pattern recognition: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 4–37 (2000)
Srihari, R.K.: Automatic indexing and content-based retrieval of captioned images. IEEE Computer 28, 49–56 (1995)
Sclaroff, S., Cascia, M.L., Sethi, S.: Unifying textual and visual cues for content-based image retrieval on the world wide web. CVIU 75, 86–98 (1999)
Paek, S., et al.: Integration of visual and text-based approaches for the content labeling and classification of photographs. In: ACM Workshop on Multim. Indexing and Retr. (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Min, P., Kazhdan, M., Funkhouser, T. (2004). A Comparison of Text and Shape Matching for Retrieval of Online 3D Models. In: Heery, R., Lyon, L. (eds) Research and Advanced Technology for Digital Libraries. ECDL 2004. Lecture Notes in Computer Science, vol 3232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30230-8_20
Download citation
DOI: https://doi.org/10.1007/978-3-540-30230-8_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23013-7
Online ISBN: 978-3-540-30230-8
eBook Packages: Springer Book Archive