Abstract
Point Pattern Matching (PPM) is an approach to establish correspondence between two related patterns by pairing up of points. PPM is widely used in the field of computer vision and pattern recognition. The existing approaches for PPM has either high computational complexity or the search space is large. To overcome this drawback, an Improved Ant Colony Optimization based Binary Search for Point Pattern Matching (IACOBSPPM) has been proposed. The algorithm chooses a query image point value from the query image point pattern and finds the reduced search space in the stored image point pattern based on the length of the point value. The query image point value is searched only in the reduced search space. When a match is found, the next query image point value of the same length is searched only from the matching position of the previous query image point value, further reducing the search space. The computational complexity of the proposal is less compared to the existing approaches for point pattern matching. Experimental results prove the efficiency of IACOBSPPM algorithm.
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
Aiger, D., Kedem, K.: Approximate input sensitive algorithms for point pattern matching. Pattern Recogn. 43, 153–159 (2010)
Li, B., Meng, Q., Holstein, H.: Point pattern matching and applications-a review. In: SMC 2003 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483), vol. 1, pp. 729–736 (2003)
Chui, H., Rangarajan, A.: A new point matching algorithm for non-rigid registration. Comput. Vis. Image Underst. 89, 114–141 (2003)
Dorigo, M., Stutzle, T.: Ant Colony Optimization, pp. 37–38. Prentice Hall of India Private Limited, New Delhi (2005)
Kang, H., Efros, A.A., Hebert, M., Kanade, T.: Image matching in large scale indoor environment. Carnegie Mellon University: School of Computer Science (2009)
Maniezzo, V., Gambardella, L.M., de Luigi, F.: Ant colony optimization (2004). http://www.idsia.ch/~luca/aco2004.pdf
Sreeja, N.K., Sankar, A.: Ant colony optimization based binary search for efficient point pattern matching in images. Eur. J. Oper. Res. 246(1), 154–169 (2015)
Sreelaja, N.K., Sreeja, N.K.: An ant colony optimization based approach for binary search. In: Tan, Y., Shi, Y. (eds.) ICSI 2021. LNCS, vol. 12689, pp. 311–321. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-78743-1_28
Van Wamelen, P.B., Li, Z., Iyengar, S.S.: A fast expected time algorithm for the 2-D point pattern matching problem. Pattern Recogn. 37, 1699–1711 (2004)
Wayman, J.L., Jain, A.K., Maltoni, D., Maio, D.: Biometric Systems: Technology, Design and Performance Evaluation. Springer London (2005). https://doi.org/10.1007/b138151
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Sreeja, N.K., Sreelaja, N.K. (2023). Improved Ant Colony Optimization Based Binary Search for Point Pattern Matching for Efficient Image Matching. In: Tan, Y., Shi, Y., Luo, W. (eds) Advances in Swarm Intelligence. ICSI 2023. Lecture Notes in Computer Science, vol 13968. Springer, Cham. https://doi.org/10.1007/978-3-031-36622-2_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-36622-2_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-36621-5
Online ISBN: 978-3-031-36622-2
eBook Packages: Computer ScienceComputer Science (R0)