메뉴 건너뛰기




Volumn 10, Issue 2, 2008, Pages 200-208

DISCOV: A framework for discovering objects in video

Author keywords

Multimedia data mining; Unsupervised learning; Video object discovery; Video segmentation

Indexed keywords

DATA MINING; MATHEMATICAL MODELS; PROBABILISTIC LOGICS; UNSUPERVISED LEARNING; VIDEO SIGNAL PROCESSING;

EID: 38349167241     PISSN: 15209210     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMM.2007.911781     Document Type: Article
Times cited : (26)

References (45)
  • 1
    • 1542315968 scopus 로고    scopus 로고
    • Object detection using the statistics of parts
    • H. Schneiderman and T. Kanade, "Object detection using the statistics of parts," Int. J. Comput. Vis., vol. 56, pp. 151-177, 2004.
    • (2004) Int. J. Comput. Vis , vol.56 , pp. 151-177
    • Schneiderman, H.1    Kanade, T.2
  • 2
    • 33745855044 scopus 로고    scopus 로고
    • The pyramid match kernel: Discriminative classification with sets of image features
    • K. Grauman and T. Darrell, "The pyramid match kernel: Discriminative classification with sets of image features," in Proc. IEEE Int. Conf. Computer Vision, 2005, pp. 1458-1465.
    • (2005) Proc. IEEE Int. Conf. Computer Vision , pp. 1458-1465
    • Grauman, K.1    Darrell, T.2
  • 3
    • 0034244889 scopus 로고    scopus 로고
    • Learning patterns of activity using real-time tracking
    • Aug
    • C. Stauffer and W. E. L. Grimson, "Learning patterns of activity using real-time tracking," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 8, pp. 747-757, Aug. 2000.
    • (2000) IEEE Trans. Pattern Anal. Mach. Intell , vol.22 , Issue.8 , pp. 747-757
    • Stauffer, C.1    Grimson, W.E.L.2
  • 4
    • 33645307063 scopus 로고    scopus 로고
    • Background and foreground modeling using non-parametric kernel density estimation for visual surveillance
    • Jul
    • A. Elgammal, R. Duraiswami, D. Harwood, and L. S. Davis, "Background and foreground modeling using non-parametric kernel density estimation for visual surveillance," Proc. IEEE, vol. 90, no. 7, pp. 1151-1163, Jul. 2002.
    • (2002) Proc. IEEE , vol.90 , Issue.7 , pp. 1151-1163
    • Elgammal, A.1    Duraiswami, R.2    Harwood, D.3    Davis, L.S.4
  • 7
    • 33846576607 scopus 로고    scopus 로고
    • Moving-object detection, association, and selection in home videos
    • Feb
    • Z. Pan and C.-W. Ngo, "Moving-object detection, association, and selection in home videos," IEEE Trans. Multimedia, vol. 9, no. 2, pp. 268-279, Feb. 2007.
    • (2007) IEEE Trans. Multimedia , vol.9 , Issue.2 , pp. 268-279
    • Pan, Z.1    Ngo, C.-W.2
  • 8
    • 0034244906 scopus 로고    scopus 로고
    • Detecting salient motion by accumulating directionally-consistent flow
    • Aug
    • L. Wixson, "Detecting salient motion by accumulating directionally-consistent flow," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 8, pp. 774-780, Aug. 2000.
    • (2000) IEEE Trans. Pattern Anal. Mach. Intell , vol.22 , Issue.8 , pp. 774-780
    • Wixson, L.1
  • 10
    • 0032098797 scopus 로고    scopus 로고
    • A unified approach to moving object detection in 2d and 3d scenes
    • Jun
    • M. Irani and P. Anandan, "A unified approach to moving object detection in 2d and 3d scenes," IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 6, pp. 577-589, Jun. 1998.
    • (1998) IEEE Trans. Pattern Anal. Mach. Intell , vol.20 , Issue.6 , pp. 577-589
    • Irani, M.1    Anandan, P.2
  • 11
  • 14
    • 0032119085 scopus 로고    scopus 로고
    • Temporal video segmentation using unsupervised clustering and semantic object tracking
    • B. Gunsel, A. Ferman, and A. Tekalp, "Temporal video segmentation using unsupervised clustering and semantic object tracking," J. Electron. Imag., vol. 7, no. 3, pp. 592-604, 1998.
    • (1998) J. Electron. Imag , vol.7 , Issue.3 , pp. 592-604
    • Gunsel, B.1    Ferman, A.2    Tekalp, A.3
  • 15
    • 0038521275 scopus 로고    scopus 로고
    • An efficient fully-unsupervised video object segmentation scheme using an adaptive neural network classifier architecture
    • May
    • A. Doulamis, K. Ntalianis, N. Doulamis, and S. Kollias, "An efficient fully-unsupervised video object segmentation scheme using an adaptive neural network classifier architecture," IEEE Trans. Neural Netw., vol. 14, no. 3, pp. 616-630, May 2003.
    • (2003) IEEE Trans. Neural Netw , vol.14 , Issue.3 , pp. 616-630
    • Doulamis, A.1    Ntalianis, K.2    Doulamis, N.3    Kollias, S.4
  • 17
    • 0345414182 scopus 로고    scopus 로고
    • Video google: A text retrieval approach to object matching in videos
    • 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.
    • (2003) Proc. IEEE Int. Conf. Computer Vision , pp. 1470-1477
    • Sivic, J.1    Zisserman, A.2
  • 18
    • 33745948591 scopus 로고    scopus 로고
    • Locus: Learning object classes with unsupervised segmentation
    • J. Winn and N. Jojic, "Locus: Learning object classes with unsupervised segmentation," in Proc. IEEE Int. Conf. Computer Vision, 2005, pp. 756-763.
    • (2005) Proc. IEEE Int. Conf. Computer Vision , pp. 756-763
    • Winn, J.1    Jojic, N.2
  • 24
    • 0034818212 scopus 로고    scopus 로고
    • Unsupervised learning by probabilistic latent semantic analysis
    • T. Hofmann, "Unsupervised learning by probabilistic latent semantic analysis," Mach. Learn., vol. 42, pp. 177-196, 2001.
    • (2001) Mach. Learn , vol.42 , pp. 177-196
    • Hofmann, T.1
  • 30
    • 84898026340 scopus 로고    scopus 로고
    • Unsupervised learning of human action categories using spatial-temporal words
    • J. C. Niebles, H. Wang, and L. Fei-Fei, "Unsupervised learning of human action categories using spatial-temporal words," in Proc. British Machine Vision Conf., 2006, pp. 814-827.
    • (2006) Proc. British Machine Vision Conf , pp. 814-827
    • Niebles, J.C.1    Wang, H.2    Fei-Fei, L.3
  • 35
    • 0002629270 scopus 로고
    • Maximum likelihood from incomplete data via the EM algorithm
    • A. P. Dempster, N. M. Laird, and D. B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," J. Roy. Statist. Soc., vol. 39, pp. 1-38, 1977.
    • (1977) J. Roy. Statist. Soc , vol.39 , pp. 1-38
    • Dempster, A.P.1    Laird, N.M.2    Rubin, D.B.3
  • 39
    • 38349159252 scopus 로고    scopus 로고
    • Available
    • [Online]. Available: http://www.robots.ox.ac.uk/~vgg/research/affine/
  • 40
    • 3042535216 scopus 로고    scopus 로고
    • Distinctive image features from scale-invariant key-points
    • D. G. Lowe, "Distinctive image features from scale-invariant key-points," Int. J. Comput. Vis., vol. 60, pp. 91-110, 2004.
    • (2004) Int. J. Comput. Vis , vol.60 , pp. 91-110
    • Lowe, D.G.1
  • 41
    • 38349112292 scopus 로고    scopus 로고
    • Extracting scale and illuminant invariant regions through color
    • R. Unnikrishnan and M. Hebert, "Extracting scale and illuminant invariant regions through color," in Proc. British Machine Vision Conf., 2006, pp. 124-138.
    • (2006) Proc. British Machine Vision Conf , pp. 124-138
    • Unnikrishnan, R.1    Hebert, M.2
  • 43
    • 77952363125 scopus 로고    scopus 로고
    • Closet+: Searching for the best strategies for mining frequent closed itemsets
    • J. Wang, J. Han, and J. Pei, "Closet+: Searching for the best strategies for mining frequent closed itemsets," in Proc. Int. Conf. Knowledge Discovery and Data Mining, 2003, pp. 236-245.
    • (2003) Proc. Int. Conf. Knowledge Discovery and Data Mining , pp. 236-245
    • Wang, J.1    Han, J.2    Pei, J.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.