Scale-Rotation Invariant Pattern Entropy for Keypoint-Based Near-Duplicate Detection

IEEE Transactions on Image Processing

Volumn 18, Issue 2, 2009, Pages 412-423

  Zhao, Wan Lei ^a   Ngo, Chong Wah ^a  

^a CITY UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Keypoints; near duplicate detection; pattern entropy (PE); visual vocabulary

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; METHODOLOGY; REPRODUCIBILITY; ROTATION; SENSITIVITY AND SPECIFICITY; VIDEORECORDING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; ROTATION; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE; VIDEO RECORDING;

EID: 62449218032     PISSN: 10577149     EISSN: 19410042     Source Type: Journal    
DOI: 10.1109/TIP.2008.2008900     Document Type: Article

References (29)

1. D. Lowe, “Distinctive image features from scale-invariant keypoints”, Int. J. Comput. Vis., vol. 60, no. 2, pp. 91–110, 2004.
2. K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, and L. V. Gool, “A comparison of affine region detectors”, Int. J. Comput. Vis., vol. 65, no. 1/2, pp. 43–72, 2005.
3. J. Zhang, M. Marszatek, S. Lazebnik, and C. Schmid, “Local features and kernels for classification of texture and object categories: A comprehensive study”, Int. J. Comput. Vis., vol. 73, no. 2, pp. 213–238, 2007.
4. Y. Ke, R. Sukthankar, and L. Huston, “Efficient near-duplicate detection and sub-image retrieval”, ACM Multimedia, pp. 869–876, 2004.
5. S. F. Chang, W. Hsu, L. Kennedy, L. Xie, A. Yanagawa, E. Zavesky, and D.-Q. Zhang, “Columbia University TRECVID-2005 video search and high-level feature extraction”, presented at the TRECVID, 2005.
6. J. Sivic and A. Zisserman, “Video Google: A text retrieval approach to object matching in videos”, in Proc. IEEE Int. Conf. Computer Vision, 2003, pp. 1470–1477.
7. A. Joly, O. Buisson, and C. Frelicot, “Content-based copy retrieval using distortion-based probabilistic similarity search”, IEEE. Trans. Multimedia, vol. 9, no. 2, pp. 293–306, Feb. 2007.
8. W.-L. Zhao, C.-W. Ngo, H.-K. Tan, and X. Wu, “Near-duplicate keyframe identification with interest point matching and pattern learning”, IEEE. Trans. Multimedia, vol. 9, no. 5, pp. 1037–1048, Aug. 2007.
9. X. Wu, A.-G. Hauptman, and C.-W. Ngo, “Practical elimination of near-duplicates from web video search”, in ACM Multimedia, 2007, pp. 218–227.
10. C.-W. Ngo, W.-L. Zhao, and Y.-G. Jiang, “Fast tracking of near-duplicate keyframes in broadcast domain with transitivity propagation”, ACM Multimedia, pp. 845–854, 2006.
11. J.-H. Hsiao, C.-S. Chen, L.-F. Chien, and M.-S. Chen, “A newapproach to image copy detection based on extended feature sets”, IEEE. Trans. Image Process., vol. 16, no. 8, pp. 2069–2079, Aug. 2007.
12. X. Wu, C.-W. Ngo, and Q. Li, “Threading and autodocumenting news videos”, IEEE Signal Process. Mag., pp. 59–68, Mar. 2006.
13. M. Shneider and S.-F. Chang, “A robust content based digital signature for image authentication”, in Proc. Int. Conf. Image Processing, 1996, vol. 3, pp. 227–230.
14. D.-Q. Zhang and S.-F. Chang, “Detecting image near-duplicate by stochastic attributed relational graph matching with learning”, ACM Multimedia, pp. 877–884, 2004.
15. K. Mikolajczyk and C. Schmid, “A performance evaluation of local descriptors”, IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 10, pp. 1615–1630, Oct. 2005.
16. K. Mikoljczyk and C. Schmid, “Scale and affine invariant interest point detectors”, Int. J. Comput. Vis., vol. 60, pp. 63–86.
17. J. Matas, O. Chum, M. Urban, and T. Pajdla, “Robust wide baseline stereo from maximally stable resgions”, in Proc. Brit. Machine Vision Conf., 2002, pp. 384–396.
18. Y. Ke and R. Sukthankar, “PCA-SIFT: A more distinctive representation for local image descriptors”, in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2004, vol. 2, pp. 506–513.
19. J. Law-To, O. Buisson, V. Gouet-Brunet, and N. Boujemaa, “Robust voting algorithm based on labels of behavior for video copy detection”, ACM Multimedia, pp. 835–844, 2006.
20. J. Sivic and A. Zisserman, “Video data mining using configurations of viewpoint invariant regions”, in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2004, pp. 488–495.
21. M. Heritier, S. Foucher, and L. Gagnon, “Key-plcaces detection and clustering in movies using latent aspects”, in Proc. Int. Conf. Image Processing, 2007, pp. 225–228.
22. M. Heritier, L. Gagnon, and S. Foucher, “Key-plcaces clustering of full-length film key-frames using latent aspects modeling over SIFT matches”, IEEE Trans. Circuits Syst. Video Technol., to be published.
23. Y. Rubner, C. Tomasi, and L. Guibas, “The earth mover's distance as a metric for image retrieval”, Int. J. Comput. Vis., vol. 40, no. 2, pp. 99–121, 2000.
24. B. Georgescu, I. Shimshoni, and P. Meer, “Mean shift based clustering in high dimensions: A texture classification example”, presented at the Int. Conf. Computer Vision, 2003.
25. [Online]. Available: http://www.ee.columbia.edu/ln/dvmm
26. [Online]. Available: http://vireo.cs.cityu.edu.hk/research/NDK/ndk.html
27. TREC Video Retrieval Evaluation (TRECVID) [Online]. Available: http://www-nlpir.nist.gov/projects/trecvid/
28. K. Mikoljczyk and C. Schmid, “An affine invariant interest point detector”, presented at the Eur. Conf. Computer Vision, 2002.
29. W.-L. Zhao, Y.-G. Jiang, and C.-W. Ngo, “Keyframe retrieval by keypoints: Can point-to-point matching help?”, in Proc. Int. Conf. on Image and Video Retrieval, 2006, pp. 72–81.