메뉴 건너뛰기




Volumn , Issue , 2008, Pages 738-745

Object mining using a matching graph on very large image collections

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING METHODS; CLUSTERING TECHNIQUES; DATA SETS; IMAGE COLLECTIONS; IMAGE DATASET; IMAGE-BASED; IMAGING CONDITIONS; LARGE IMAGES; MATCHING GRAPHS; PARTIAL OCCLUSIONS; POTENTIAL APPLICATIONS; SCALABLE METHODS; SPATIAL CONSISTENCIES; WORK FOCUS;

EID: 65249182449     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICVGIP.2008.103     Document Type: Conference Paper
Times cited : (76)

References (32)
  • 1
    • 84868926631 scopus 로고    scopus 로고
    • http://www.robots.ox.ac.uk/̃vgg/data/oxbuildings/.
  • 4
    • 65249151755 scopus 로고    scopus 로고
    • Web scale image clustering: Large scale discovery of spatially related images
    • Technical Report CTU-CMP-2008-15, Czech Technical University in Prague
    • O. Chum and J. Matas. Web scale image clustering: Large scale discovery of spatially related images. Technical Report CTU-CMP-2008-15, Czech Technical University in Prague, 2008.
    • (2008)
    • Chum, O.1    Matas, J.2
  • 6
    • 50649105215 scopus 로고    scopus 로고
    • Total recall: Automatic query expansion with a generative feature model for object retrieval
    • O. Chum, J. Philbin, J. Sivic, M. Isard, and A. Zisserman. Total recall: Automatic query expansion with a generative feature model for object retrieval. In Proc. ICCV, 2007.
    • (2007) Proc. ICCV
    • Chum, O.1    Philbin, J.2    Sivic, J.3    Isard, M.4    Zisserman, A.5
  • 8
    • 33745155436 scopus 로고    scopus 로고
    • A Bayesian hierarchical model for learning natural scene categories
    • Jun
    • L. Fei-Fei and P. Perona. A Bayesian hierarchical model for learning natural scene categories. In Proc. CVPR, Jun 2005.
    • (2005) Proc. CVPR
    • Fei-Fei, L.1    Perona, P.2
  • 9
    • 33845575890 scopus 로고    scopus 로고
    • Unsupervised learning of categories from sets of partially matching image features
    • K. Grauman and T. Darrell. Unsupervised learning of categories from sets of partially matching image features. In Proc. CVPR, 2006.
    • (2006) Proc. CVPR
    • Grauman, K.1    Darrell, T.2
  • 10
    • 0034818212 scopus 로고    scopus 로고
    • Unsupervised learning by probabilistic latent semantic analysis
    • T. Hofmann. Unsupervised learning by probabilistic latent semantic analysis. Machine Learning, 43:177-196, 2001.
    • (2001) Machine Learning , vol.43 , pp. 177-196
    • Hofmann, T.1
  • 11
    • 65249117543 scopus 로고    scopus 로고
    • Modeling and recognition of landmark image collections using iconic scene graphs
    • X. Li, C. Wu, C. Zach, S. Lazebnik, and J.-M. Frahm. Modeling and recognition of landmark image collections using iconic scene graphs. In Proc. ECCV, 2008.
    • (2008) Proc. ECCV
    • Li, X.1    Wu, C.2    Zach, C.3    Lazebnik, S.4    Frahm, J.-M.5
  • 12
    • 3042535216 scopus 로고    scopus 로고
    • Distinctive image features from scale-invariant keypoints
    • D. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60(2):91-110, 2004.
    • (2004) IJCV , vol.60 , Issue.2 , pp. 91-110
    • Lowe, D.1
  • 13
    • 9644260534 scopus 로고    scopus 로고
    • Scale&affine invariant interest point detectors
    • K. Mikolajczyk and C. Schmid. Scale&affine invariant interest point detectors. IJCV, 1(60):63-86, 2004.
    • (2004) IJCV , vol.1 , Issue.60 , pp. 63-86
    • Mikolajczyk, K.1    Schmid, C.2
  • 14
    • 42749100809 scopus 로고    scopus 로고
    • Fast algorithm for detecting community structure in networks
    • M. Newman. Fast algorithm for detecting community structure in networks. Physical Review E, 69, 2004.
    • (2004) Physical Review E , vol.69
    • Newman, M.1
  • 15
    • 37649028224 scopus 로고    scopus 로고
    • Finding and evaluating community structure in networks
    • M. Newman and M. Girvan. Finding and evaluating community structure in networks. Physical Review E, 69, 2004.
    • (2004) Physical Review E , vol.69
    • Newman, M.1    Girvan, M.2
  • 16
    • 84899013108 scopus 로고    scopus 로고
    • On spectral clustering: Analysis and an algorithm
    • A. Ng, M. Jordan, and Y. Weiss. On spectral clustering: Analysis and an algorithm. In NIPS, 2002.
    • (2002) NIPS
    • Ng, A.1    Jordan, M.2    Weiss, Y.3
  • 17
    • 33845592987 scopus 로고    scopus 로고
    • Scalable recognition with a vocabulary tree
    • D. Nister and H. Stewenius. Scalable recognition with a vocabulary tree. In Proc. CVPR, 2006.
    • (2006) Proc. CVPR
    • Nister, D.1    Stewenius, H.2
  • 18
  • 20
    • 36849085858 scopus 로고    scopus 로고
    • Video mining with frequent itemset configurations
    • T. Quack, V. Ferrari, and L. Van Gool. Video mining with frequent itemset configurations. In Proc. CIVR, 2006.
    • (2006) Proc. CIVR
    • Quack, T.1    Ferrari, V.2    Van Gool, L.3
  • 21
    • 70349152349 scopus 로고    scopus 로고
    • World-scale mining of objects and events from community photo collections
    • T. Quack, B. Leibe, and L. Van Gool. World-scale mining of objects and events from community photo collections. In Proc. CIVR, 2008.
    • (2008) Proc. CIVR
    • Quack, T.1    Leibe, B.2    Van Gool, L.3
  • 23
    • 84944398910 scopus 로고    scopus 로고
    • Multi-view matching for unordered image sets, or How do I organize my holiday snaps?
    • Springer-Verlag
    • F. Schaffalitzky and A. Zisserman. Multi-view matching for unordered image sets, or "How do I organize my holiday snaps?". In Proc. ECCV, volume 1, pages 414-431. Springer-Verlag, 2002.
    • (2002) Proc. ECCV , vol.1 , pp. 414-431
    • Schaffalitzky, F.1    Zisserman, A.2
  • 25
    • 50949090870 scopus 로고    scopus 로고
    • Scene summarization for online image collections
    • I. Simon, N. Snavely, and S. M. Seitz. Scene summarization for online image collections. In Proc. ICCV, 2007.
    • (2007) Proc. ICCV
    • Simon, I.1    Snavely, N.2    Seitz, S.M.3
  • 27
    • 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. ICCV, 2003.
    • (2003) Proc. ICCV
    • Sivic, J.1    Zisserman, A.2
  • 28
    • 5044234908 scopus 로고    scopus 로고
    • Video data mining using configurations of viewpoint invariant regions
    • Jun
    • J. Sivic and A. Zisserman. Video data mining using configurations of viewpoint invariant regions. In Proc. CVPR, Jun 2004.
    • (2004) Proc. CVPR
    • Sivic, J.1    Zisserman, A.2
  • 29
    • 77954006739 scopus 로고    scopus 로고
    • Photo tourism: Exploring photo collections in 3D
    • N. Snavely, S. Seitz, and R. Szeliski. Photo tourism: exploring photo collections in 3D. In Proc. ACM SIGGRAPH, pages 835-846, 2006.
    • (2006) Proc. ACM SIGGRAPH , pp. 835-846
    • Snavely, N.1    Seitz, S.2    Szeliski, R.3
  • 30
    • 0347418978 scopus 로고    scopus 로고
    • Implicitly restarted arnoldi/lanczos methods for large scale eigenvalue calculations
    • Technical report
    • D. Sorensen. Implicitly restarted arnoldi/lanczos methods for large scale eigenvalue calculations. Technical report, 1996.
    • (1996)
    • Sorensen, D.1
  • 31
    • 84880106426 scopus 로고    scopus 로고
    • S. White and P. Smyth. A spectral clustering approach to finding communities in graphs. In SDM, 2005.
    • S. White and P. Smyth. A spectral clustering approach to finding communities in graphs. In SDM, 2005.
  • 32
    • 65249100555 scopus 로고    scopus 로고
    • A large-scale system for searching and browsing images from the world wide web
    • A. Yavlinksy, D. Heesch, and S. Rueger. A large-scale system for searching and browsing images from the world wide web. In CIVR, 2005.
    • (2005) CIVR
    • Yavlinksy, A.1    Heesch, D.2    Rueger, S.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.