Abstract
Detection and localization of unspecified similar fragments in random images is one of the most challenging problems in CBVIR (classic techniques focusing on full-image or sub-image retrieval usually fail in such a problem). We propose a new method for near-duplicate image fragment matching using a topology-based framework. The method works on visual data only, i.e. no semantics or a’priori knowledge is assumed. Near-duplicity of image fragments is modeled by topological constraints on sets of matched keypoints (instead of geometric constrains typically used in image matching). The paper reports a time-efficient (i.e. capable of working in real time with a video input) implementation of the proposed method. The application can be run using a mid-range personal computer and a medium-quality video camera.
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
Wang, J.Z., Li, J., Wiederhold, G.: Simplicity: Semantics-sensitive integrated matching for picture libraries. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 947–963 (2001)
Islam, M., Sluzek, A.: Relative scale method to locate an object in cluttered environment. Image and Vision Computing 26, 259–274 (2008)
Lowe, D.: Object recognition from local scale-invariant features. In: ICCV 1999, vol. 2, pp. 1150–1157 (1999)
Xiao, J., Shah, M.: Two-frame wide baseline matching. In: ICCV 2003, vol. 1, pp. 603–609. IEEE, Los Alamitos (2003)
Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)
Chiu, H.P., Lozano-Perez, T.: Matching interest points using affine invariant concentric circles. In: Proceedings of the 18th International Conference on Pattern Recognition, vol. 2, pp. 167–170 (2006)
Mudigonda, P.K., Jawahar, C.V., Narayanan, P.J.: Geometric structure computation from conics. In: Fourth Indian Conference on Computer Vision, Graphics and Image Processing (2004)
Kannala, J., Salo, M., Heikkila, J.: Algorithms for computing a planar homography from conics in correspondence. In: British Machine Vision Conference (2006)
Tuytelaars, T., Gool, L.V.: Wide baseline stereo matching based on local, affinely invariant regions. In: Proc. of British Machine Vision Conference BMVC 2000, pp. 412–425 (2000)
Yang, D., Sluzek, A.: A low-dimensional local descriptor incorporating tps warping for image matching. Image and Vision Computing 28(8), 1184–1195 (2010)
Zhao, W.L., Ngo, C.W.: Scale-rotation invariant pattern entropy for keypoint-based near-duplicate detection. IEEE Transactions on Image Processing 18, 412–423 (2009)
Heritier, M., Foucher, S., Gagnon, L.: Key-places detection and clustering in movies using latent aspects. In: Proc. 14th IEEE Int. Conf. Image Processing, vol. 2, pp. II.225–II.228 (2007)
Chum, O., Perdoch, M., Matas, J.: Geometric min-hashing: Finding a (thick) needle in a haystack. In: Proc. of IEEE Conf. CVPR 2009, pp. 17–24 (2009)
Fischler, M., Bolles, R.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. In: Buxton, B.F., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1064, pp. 683–695. Springer, Heidelberg (1996)
Rother, C., Kolmogorov, V., Minka, T., Blake, A.: Cosegmentation of image pairs by histogram matching – incorporating a global constraint into mrfs. In: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 993–1000 (2006)
Mukherjee, L., Singh, V., Dyer, C.: R.: Half-integrality based algorithms for cosegmentation of images. In: Proc. of IEEE Conf. CVPR 2009, pp. 2028–2035 (2009)
Paradowski, M., Śluzek, A.: Local keypoints and global affine geometry: triangles and ellipses for image fragment matching. In: Kwasnicka, H., Jain, L. (eds.) Innovations in Intelligent Image Analysis. Springer, Heidelberg (in print, 2010)
Paradowski, M., Śluzek, A.: Detection of image fragments related by affine transforms: Matching triangles and ellipses. In: Proc. of ICISA 2010, Seul (2010)
Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. Int. J. Comput. Vision 60, 63–86 (2004)
Matas, J., Schum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. In: British Machine Vision Conference, pp. 384–393 (2002)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. PAMI 27, 1615–1630 (2005)
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Computer Vision and Image Understanding 110, 346–359 (2008)
Cornelis, N., Gool, L.V.: Fast scale invariant feature detection and matching on programmable graphics hardware. In: CVPR 2008 Workshop (2008)
Śluzek, A., Paradowski, M.: A vision-based technique for assisting visually impaired people and autonomous agents. In: Proc. 3rd Int. Conf. Human System Interaction HSI 2010, Rzeszow (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Śluzek, A., Paradowski, M. (2010). Real-Time Retrieval of Near-Duplicate Fragments in Images and Video-Clips. In: Blanc-Talon, J., Bone, D., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2010. Lecture Notes in Computer Science, vol 6474. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17688-3_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-17688-3_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17687-6
Online ISBN: 978-3-642-17688-3
eBook Packages: Computer ScienceComputer Science (R0)