Abstract
An algorithm that utilizes the similarity comparison is proposed to get more proper match result, which is easy to implement. SIFT depends on principal direction which will lead to low precision rate when the direction is incorrectly computed. In this paper, similarities are tested by cosine theorem of matched points in some area to find stable matches and exclude mismatches (push) at first. Part of correct matches in excluded points are revived (pull) through stable matches, which are located in cluster sets centered by stable matched points, thus shrink search field and boosting the algorithm. Sum of Square Distance (SSD) measurement function is tested and chosen as similarity function to accomplish the reviving step. Experimental results show that the proposed method exhibits improved performance compared with SIFT and other methods.
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
References
Thirion, J.-P.: Image matching as a diffusion process: an analogy with Maxwell’s demons. Med. Image Anal. 2(3), 243–260 (1998)
Cho, M., Lee, K.M.: Progressive graph matching: making a move of graphs via probabilistic voting. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 398–405. IEEE (2012)
Berg, A.C., Berg, T.L., Malik, J.: Shape matching and object recognition using low distortion correspondences. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, CVPR 2005. IEEE (2005)
Hirschmuller, H., Scharstein, D.: Evaluation of cost functions for stereo matching. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007. IEEE (2007)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)
He, L., Wang, S., Pappas, T.N.: 3D surface registration using Z-SIFT. In: ICIP 2011, pp. 1985–1988 (2011)
Mazin, B., Delon, J., Gousseau, Y.: Combining color and geometry for local image matching. In: 21st International Conference on Pattern Recognition (ICPR), pp. 2667–2680. IEEE. (2012)
Ishii, J., et al.: Wide-baseline stereo matching using ASIFT and POC. In: 19th IEEE International Conference on Image Processing (ICIP), pp. 2977–2980. IEEE (2012)
Lee, J., Cho, M., Lee, K.M.: Hyper-graph matching via reweighted random walks. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1633–1640. IEEE (2011)
Collins, R.T., Beveridge, J.R.: Matching perspective views of coplanar structures using projective unwarping and similarity matching. In: 1993 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Proceedings CVPR 1993. IEEE (1993)
Adjeroh, D.A., Lee, M.-C., King, I.: A distance measure for video sequence similarity matching. In: International Workshop on Multi-Media Database Management Systems, Proceedings. IEEE (1998)
Lim, J.-H., et al.: Learning similarity matching in multimedia content-based retrieval. IEEE Trans. Knowl. Data Eng. 13(5), 846–850 (2001)
Ke, Y., Sukthankar, R.: PCA-SIFT: a more distinctive representation for local image descriptors. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, pp. 506–513. IEEE (2004)
Scharstein, D., Szeliski, R.: High-accuracy stereo depth maps using structured light. In: 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003, Proceedings. IEEE (2003)
Lin, H.-Y., Chou, X.-H.: Stereo matching on low intensity quantization images. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp. 2618–2621. IEEE (2012)
Middlebury. http://vision.middlebury.edu/stereo
Acknowledgments
The research is supported by National Natural Science Foundation of China(61171184, 61201309).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Yu, D., Ye, Z., Zhao, W., Tang, X. (2015). Precise Image Matching: A Similarity Measure Approach. In: He, X., et al. Intelligence Science and Big Data Engineering. Image and Video Data Engineering. IScIDE 2015. Lecture Notes in Computer Science(), vol 9242. Springer, Cham. https://doi.org/10.1007/978-3-319-23989-7_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-23989-7_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23987-3
Online ISBN: 978-3-319-23989-7
eBook Packages: Computer ScienceComputer Science (R0)