Abstract
Image matching has become a hot topic of research in image processing, image retrieval and related fields. The SIFT (Scale Invariant Feature Transform) is a robust learning algorithm for extracting local features. However, at present several problems still exist in destroying the original data of internal spatial structure, and it will result in the curse of dimensionality. In this paper, we will present an algorithm based on 2DPCA-SIFT, which utilizes the original two-dimensional image to construct covariance matrix. From the experimental result we can see, the proposed algorithm integrally retains the image information of two-dimensional structure, and has higher matching accuracy.
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
Moravec, H.: Towards automatic visual obstacle avoidance. In: Processing of the 5th International Joint Conference on Artificial Intelligence, p. 584 (1977)
Harris, C., Stephens, M.: A combined corner and edge detector. In: The Alvey Vision Conference, pp. 147–151 (1988)
Lowe, D.G.: Object recognition from local scale-invariant features. In: International Conference on Computer Vision, pp. 1150–1157 (1999)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Ke, Y., Sukthankar, R.: PCA-SIFT: a more distinctive representation for local image descriptors. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 511–517 (2004)
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded Up Robust Features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)
Cui, C., Ngan, K.: Scale- and Affine-Invariant Fan Feature. IEEE Transactions on Image Processing 20(6), 1627–1640 (2011)
Yao, S., Guturu, P., Buckles, B.P.: Wireless Capsule Endoscopy Video Segmentation Using an Unsupervised Learning Approach Based on Probabilistic Latent Semantic Analysis With Scale Invariant Features. IEEE Transactions on Information Technology in Biomedicine 16(1), 98–105 (2011)
Wang, S., You, H., Fu, K.: BFSIFT: A Novel Method to Find Feature Matches for SAR Image Registration. Geoscience and Remote Sensing Letters 9(4), 1–5 (2011)
Goncalves, H., Corte-Real, L., Goncalves, J.A.: Automatic Image Registration Through Image Segmentation and SIFT. IEEE Transactions on Geoscience and Remote Sensing 49(7), 2589–2600 (2011)
Yang, J., Zhang, D., Frangi, A.F.: Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(1), 131–137 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, K.L., Wu, Q., Wang, Z., Zhang, J., Meng, Q. (2012). Image Matching Based on 2DPCA-SIFT. In: Wang, F.L., Lei, J., Lau, R.W.H., Zhang, J. (eds) Multimedia and Signal Processing. CMSP 2012. Communications in Computer and Information Science, vol 346. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35286-7_47
Download citation
DOI: https://doi.org/10.1007/978-3-642-35286-7_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35285-0
Online ISBN: 978-3-642-35286-7
eBook Packages: Computer ScienceComputer Science (R0)