Abstract
Multi-view correspondence of wide-baseline image matching is still a challenge task in computer vision. There are two main steps in dealing with correspondence issue: feature description and similarity search. The well-known SIFT descriptor is shown to be a-state-of-art descriptor which could keep distinctive invariant under transformation, large scale changes, noises and even small view point changes. This paper uses the SIFT as feature descriptor, and proposes a new search algorithm for similarity search. The proposed dichotomy based hash (DBH) method performs better than the widely used BBF (Best Bin First) algorithm, and also better than LSH (Local Sensitive Hash). DBH algorithm can obtain much higher (1-precision)-recall ratio in different kinds of image pairs with rotation, scale, noises and weak affine changes. Experimental results show that DBH can obviously improve the search accuracy in a shorter time, and achieve a better coarse match result.
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
Brown, M., Szeliski, R., Samarin, A.: Multi-Image Matching using Multi-Scale Oriented Patches. In: CVPR, pp. 510–517 (2005)
Lowe, D.G.: Distinctive Image Features from Scale-Invariant Key points. International Journal of Computer Vision 60(2), 91–110 (2004)
Ke, Y., Sukthankar, R., Huston, L.: Efficient Near duplicate Detection and Sub-image Retrieval. In: ACM. International Conference on Multimedia (2004)
Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: exploring photo collections in 3d. ACM Trans. Graph. 25(3), 835–846 (2006)
Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. In: IJCV, pp. 63–86 (2004)
Omercevic, D., Drbohlav, O., Leonardis, A.: High-Dimensional Feature Matching: Employing the Concept of Meaningful Nearest Neighbors. In: ICCV (2007)
Mikolajczyk, K., Schmid, C.: A Performance Evaluation of Local Descriptors. IEEE Transactions on PAMI, 1615–1630 (2005)
Mikolajczyk, K., Matas, J.: Improving Descriptors for Fast Tree Matching by Optimal Linear Projection. In: Proc. ICCV (2007)
Ke, Y., Sukthankar, R.: PCA-SIFT: A More Distinctive Representation for Local Image Descriptors. In: CVPR, pp. 511–517 (2004)
Jagadish, H.V., Ooi, B.C., et al.: iDistance: An adaptive B + 2tree based indexing method for nearest neighbor search. ACM Trans. on Data Base Systems, 364–397 (2005)
Beis, J.S., Lowe, D.G.: Shape Indexing Using Approximate Nearest-Neighbor Search in High-Dimensional Spaces. In: CVPR, pp. 1000–1006 (1997)
Indyk, P., Motwaniy, R.: Approximate Nearest Neighbors Towards Removing the Curse of Dimensionality. In: 30th ACM Symp. on Theory of Computing, pp. 604–612 (1998)
Gionis, A., Indyky, P., Motwaniz, R.: Similarity Search in High Dimensions via Hashing. The VLDB Journal, 518–529 (1999)
Liu, T., Moore, A.W., Gray, A., Yang, K.: An Investigation of Practical Approximate Nearest Neighbor Algorithm. In: NIPS, pp. 825–832 (2004)
Nene, S.A., Nayar, S.K.: A Simple Algorithm for Nearest Neighbor Search in High Dimensions. IEEE Transactions on PAMI 19(9), 989–1003 (1997)
http://www.lear.inrialpes.fr /people/Mikolajczyk
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
He, Z., Wang, Q. (2008). A Fast and Effective Dichotomy Based Hash Algorithm for Image Matching. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2008. Lecture Notes in Computer Science, vol 5358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89639-5_32
Download citation
DOI: https://doi.org/10.1007/978-3-540-89639-5_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89638-8
Online ISBN: 978-3-540-89639-5
eBook Packages: Computer ScienceComputer Science (R0)