Abstract
This paper presents a new method to combine local and global features for near-duplicate images detection. It mainly consists of three steps. Firstly, the keypoints of images are extracted and preliminarily matched. Secondly, the matched keypoints are voted for estimation of affine transform to reduce false matching keypoints. Finally, to further confirm the matching, the Local Binary Pattern (LBP) and color histograms of areas formed by matched keypoints in two images are compared. This method has the advantage for handling the case when there are only a few matched keypoints. The proposed algorithm has been tested on Columbia dataset and compared quantitatively with the RANdom SAmple Consensus (RANSAC) and the Scale-Rotation Invariant Pattern Entropy (SR-PE) methods. The results turn out that the proposed method compares favorably against the state-of-the-arts.
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.
Similar content being viewed by others
References
Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust Wide Baseline Stereo from Maximally Stable Regions. In: British Machine Vision Conferene, pp. 384–396 (2002)
Mikoljczyk, K., Schmid, C.: Scale and Affine Invariant Interest Point Detectors. International Journal of Computer Vision 60, 63–86 (2004)
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: CVPR, vol. 2, pp. 506–513 (2004)
Ke, Y., Sukthankar, R., Huston, L.: Efficient Near-Duplicate Detection and Sub-Image Retrieval. In: ACM Multimedia, pp. 869–876 (2004)
Zhao, W.L., Ngo, C.W., Tan, H.K., Wu, X.: Near-Duplicate Keyframe Identification with Interest Point Matching and Pattern Learning. IEEE. Trans. on Multimedia 9(5), 1037–1048 (2007)
Ngo, C.W., Zhao, W.L., Jiang, Y.G.: Fast Tracking of Near-Duplicate Keyframes in Broadcast Domain with Transitivity Propagation. In: ACM Multimedia, pp. 845–854 (2006)
Zhao, W.L., Ngo, C.W.: Scale-Rotation Invariant Pattern Entropy for Keypoint-based Near-Duplicate Detection. IEEE Trans. on Image Processing 18(2), 412–423 (2009)
Zhu, J., Hoi, S.C.H., Lyu, M.R., Yan, S.: Near-Duplicate Keyframe Retrieval by Nonrigid Image Matching. In: MM 2008, pp. 41–50 (2008)
Qamra, A., Meng, Y., Chang, E.Y.: Enhanced Perceptual Distance Functions and Indexing for Image Replica Recognition. PAMI 27(3), 379–391 (2005)
Zhang, D.Q., Chang, S.F.: Detecting Image Near-Duplicate by Stochastic Attributed Relational Graph Matching with Learning. In: ACM MM 2004, pp. 877–884 (2004)
Zhao, W., Jiang, Y., Ngo, C.: Keyframe Retrieval by Keypoints: Can Point-to-point Matching Help? In: CIVR 2006, pp. 72–81 (2006)
Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Kadir, F.S.T., Gool, L.V.: A Comparison of Affine Region Detectors. International Journal of Computer Vision 65(1/2), 43–72 (2005)
Mikolajczyk, K., Schmid, C.: A Performance Evaluation of Local Descriptors. IEEE Trans. on Pattern Analysis and Machine Intelligence 27(10), 1615–1630 (2005)
Fleck, D., Duric, Z.: Affine Invariant-Based Classification of Inliers and Outliers for Image Matching. In: Kamel, M.S., Campilho, A.C. (eds.) ICIAR 2005. LNCS, vol. 3656, pp. 407–414. Springer, Heidelberg (2005)
TREC Video Retrieval Evaluation (TRECVID) (2010), http://trecvid.nist.gov/
Ojala, T., Pietikainen, M.: Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(7), 791–981 (2002)
Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, London (1990)
http://www.csse.uwa.edu.au/~pk/Research/MatlabFns/index.html (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, Y., Hou, Z., Leman, K., Pham, N.T., Chua, T., Chang, R. (2011). Combination of Local and Global Features for Near-Duplicate Detection. In: Lee, KT., Tsai, WH., Liao, HY.M., Chen, T., Hsieh, JW., Tseng, CC. (eds) Advances in Multimedia Modeling. MMM 2011. Lecture Notes in Computer Science, vol 6523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17832-0_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-17832-0_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17831-3
Online ISBN: 978-3-642-17832-0
eBook Packages: Computer ScienceComputer Science (R0)