Abstract
To provide satisfactory accuracy and flexibility, most of the existing shape retrieval methods make use of different alignments and translations of the objects that introduce much computational complexity. The most computationally expensive part of these algorithms is measuring the degree of match (or mismatch) of the query object with the objects stored in database. In this paper, we present an approach to cut down a large portion of this search space (number of objects in database) that retrieval algorithms need to take into account. This method is applicable in clustering based approaches also. Moreover, this minimization is done keeping the accuracy of the retrieval algorithms intact and its efficiency is not severely affected in high dimensionalities.
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
Greenspan, M., Godin, G., Talbot, J.: Acceleration of binning nearest neighbor methods. In: Vision Interface, Montreal, Canada, pp. 337–344 (2000)
Greenspan, M., Godin, G.: A Nearest Neighbor Method for Efficient ICP. In: 3DIM01: Proceedings of the 3rd International Conference on 3-D Digital Imaging and Modeling, Quebec City, Quebec, Canada (2001)
Flickner, M., Sawhney, H., Niblack, W., Ashley, J., Huang, Q., Dom, B., Gorkani, M., Hafner, J., Lee, D., Petkovic, D., Steele, D., Yanker, P.: Query by image and video content: The QBIC system. IEEE Computer 28, 23–32 (1995)
Zhu, S.C., Yuille, A.L.: FORMS: A flexible object recognition and modelling system. Internat. J. Computer Vision 20(3), 187–212 (1996)
Chen, S.W., Tung, S.T., Fang, C.Y., Cherng, S., Jain, A.K.: Extended attributed string matching for shape recognition. Computer Vision and Image Understanding 70(1), 36–50 (1998)
Gdalyahu, Y., Weinshall, D.: Flexible syntactic matching of curves and its application to automatic hierarchical classification of silhouettes. IEEE Trans. Pattern Analysis and Machine Intell. 21(12), 1312–1328 (1999)
Latecki, L., Lak€amper, R.: Shape similarity measure based on correspondence of visual parts. IEEE Trans. Pattern Anal. Machine Intell. 22(10), 1185–1190 (2000)
Stein, F., Medioni, G.: Structural indexing: efficient 2-D object recognition. IEEE Trans. Pattern Anal. Machine Intell. 14(12), 1198–1204 (1992)
Super, B.J.: Fast retrieval of isolated visual shapes. Computer Vision and Image Understanding 85(1), 1–21 (2002)
Chuang, G.C.-H., Kuo, C.-C.J.: Wavelet descriptor of planar curves: theory and applications. IEEE Transactions Image Process 5(1), 56–70 (1996)
Mokhtarian, F., Abbasi, S., Kittler, J.: Efficient and robust retrieval by shape content through curvature scale space. In: Smeulders, A., Jain, R. (eds.) Image Databases and Multi-Media Search, pp. 51–58. World Scientific, New Jersey (1997)
Weber, R., Schek, H.-J., Blott, S.: A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces. In: Widom, J., Gupta, A., Shmueli, O. (eds.) VLDB 1998, Proceedings of 24th International Conference on Very Large Data Bases, pp. 24–27 (1998)
Li, W., Salari, E.: Successive Elimination Algorithm for Motion Estimation. IEEE transactions on image processing 4(1), 105–107 (1995)
Salari, E., Li, W.: A Fast Quadtree Motion Segmentation for Image Sequence Coding. J. Signal Processing: Image Communication 14, 811–816 (1999)
Santini, S., Jain, R.: Similarity measures. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(9), 871–883 (1999)
Super, B.J.: Fast Correspondence-based System for Shape Retrieval. Pattern Recognition Letters 25(2), 217–225 (2004)
Beyer, K.S., Goldstein, J., Ramakrishnan, R., Shaft, U.: When Is ‘Nearest Neighbor’ Meaningful? In: Beeri, C., Bruneman, P. (eds.) ICDT 1999. LNCS, vol. 1540, pp. 217–235. Springer, Heidelberg (1998)
Berchtold, S., Böhm, C., Braunmüller, B., Keim, D.A., Kriegel, H.P.: Fast parallel similarity search in multimedia databases. In: Proc. of the ACM SIGMOD Int. Conf. on Management of Data, Tucson, USA, pp. 1–12 (1997)
Berchtold, S., Keim, D., Kriegel, H.P.: The X-tree: An index structure for high_dimensional data. In: Proc. of the Int. Conference on Very Large Databases, pp. 28–39 (1996)
Katayama, N., Satoh, S.: The SR-tree: An index structure for high-dimensional nearest neighbor queries. In: Proc. of the ACM SIGMOD Int. Conf. on Management of Data, Tucson, USA, pp. 369–380 (1997)
Chen, J.-Y., Bouman, C.A., Allebach, J.P.: Fast image database search using tree-structured vq. In: Proceedings of the International Conference on Image Processing, Santa Barbara, CA, October 1997, pp. 26–29 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Abdullah-Al-Wadud, M., Chae, O. (2006). Minimizing the Search Space for Shape Retrieval Algorithms. In: Levi, A., Savaş, E., Yenigün, H., Balcısoy, S., Saygın, Y. (eds) Computer and Information Sciences – ISCIS 2006. ISCIS 2006. Lecture Notes in Computer Science, vol 4263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11902140_13
Download citation
DOI: https://doi.org/10.1007/11902140_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47242-1
Online ISBN: 978-3-540-47243-8
eBook Packages: Computer ScienceComputer Science (R0)