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Volumn , Issue , 2009, Pages 2254-2261

Shape discovery from unlabeled image collections

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING ALGORITHMS; CLUTTER (INFORMATION THEORY);

EID: 70450169648     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPRW.2009.5206698     Document Type: Conference Paper
Times cited : (56)

References (29)
  • 1
    • 0003916202 scopus 로고    scopus 로고
    • Shape context: A new descriptor for shape matching and object recognition
    • S. Belongie, J. Malik, and J. Puzicha. Shape Context: A New Descriptor for Shape Matching and Object Recognition. In NIPS, 2000.
    • (2000) NIPS
    • Belongie, S.1    Malik, J.2    Puzicha, J.3
  • 2
    • 24644502276 scopus 로고    scopus 로고
    • Shape matching and object recognition low distortion correspondences
    • June
    • A. Berg, T. Berg, and J. Malik. Shape Matching and Object Recognition Low Distortion Correspondences. In CVPR, June 2005.
    • (2005) CVPR
    • Berg, A.1    Berg, T.2    Malik, J.3
  • 3
    • 0023776774 scopus 로고
    • Surface vs. Edge-based determinants of visual recognition
    • I. Biederman and G. Ju. Surface vs. Edge-Based Determinants of Visual Recognition. Cognitive Psychology, 20:38-64, 1988.
    • (1988) Cognitive Psychology , vol.20 , pp. 38-64
    • Biederman, I.1    Ju, G.2
  • 5
    • 50649119439 scopus 로고    scopus 로고
    • Non-metric affinity propagation for unsupervised image categorization
    • D. Dueck and B. Frey. Non-metric Affinity Propagation for Unsupervised Image Categorization. In ICCV, 2007.
    • (2007) ICCV
    • Dueck, D.1    Frey, B.2
  • 6
    • 33745155436 scopus 로고    scopus 로고
    • A bayesian hierarchical model for learning natural scene categories
    • L. Fei-Fei and P. Perona. A Bayesian Hierarchical Model for Learning Natural Scene Categories. In CVPR, 2005.
    • (2005) CVPR
    • Fei-Fei, L.1    Perona, P.2
  • 7
    • 34948830186 scopus 로고    scopus 로고
    • Hierarchical matching of deformable shapes
    • P. Felzenszwalb and J. Schwartz. Hierarchical Matching of Deformable Shapes. In CVPR, 2007.
    • (2007) CVPR
    • Felzenszwalb, P.1    Schwartz, J.2
  • 8
    • 24644456520 scopus 로고    scopus 로고
    • A visual category filter for google images
    • R. Fergus, P. Perona, and A. Zisserman. A Visual Category Filter for Google Images. In ECCV, 2004.
    • (2004) ECCV
    • Fergus, R.1    Perona, P.2    Zisserman, A.3
  • 9
    • 34948828581 scopus 로고    scopus 로고
    • Accurate object detection with deformable shape models learnt from images
    • V. Ferrari, F. Jurie, and C. Schmid. Accurate Object Detection with Deformable Shape Models Learnt from Images. In CVPR, 2007.
    • (2007) CVPR
    • Ferrari, V.1    Jurie, F.2    Schmid., C.3
  • 10
    • 34948832159 scopus 로고    scopus 로고
    • Object detection by contour segment networks
    • V. Ferrari, T. Tuytelaars, and L. V. Gool. Object Detection by Contour Segment Networks. In ECCV, 2006.
    • (2006) ECCV
    • Ferrari, V.1    Tuytelaars, T.2    Gool, L.V.3
  • 11
    • 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 CVPR, 2006.
    • (2006) CVPR
    • Grauman, K.1    Darrell, T.2
  • 12
    • 51949105707 scopus 로고    scopus 로고
    • Unsupervised modeling of object categories using link analysis techniques
    • G. Kim, C. Faloutsos, and M. Hebert. Unsupervised Modeling of Object Categories Using Link Analysis Techniques. In CVPR, 2008.
    • (2008) CVPR
    • Kim, G.1    Faloutsos, C.2    Hebert, M.3
  • 15
    • 70450205005 scopus 로고    scopus 로고
    • Foreground focus: Unsupervised learning from partially matching images
    • Y. J. Lee and K. Grauman. Foreground Focus: Unsupervised Learning From Partially Matching Images. In BMVC, 2008.
    • (2008) BMVC
    • Lee, Y.J.1    Grauman., K.2
  • 17
    • 50649084971 scopus 로고    scopus 로고
    • Unsupervised image categorization and object localization using topic models and correspondences between images
    • D. Liu and T. Chen. Unsupervised Image Categorization and Object Localization using Topic Models and Correspondences between Images. In ICCV, 2007.
    • (2007) ICCV
    • Liu, D.1    Chen, T.2
  • 18
    • 3042535216 scopus 로고    scopus 로고
    • Distinctive image features from scale-invariant keypoints
    • D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints. IJCV, 60(2), 2004.
    • (2004) IJCV , vol.60 , Issue.2
    • Lowe, D.1
  • 19
    • 3042525106 scopus 로고    scopus 로고
    • Learning to detect natural image boundaries using local brightness, color, and texture cues
    • May
    • D. Martin, C. Fowlkes, and J. Malik. Learning to Detect natural Image Boundaries Using Local Brightness, Color, and Texture Cues. TPAMI, 26(5):530-549, May 2004.
    • (2004) TPAMI , vol.26 , Issue.5 , pp. 530-549
    • Martin, D.1    Fowlkes, C.2    Malik, J.3
  • 20
    • 0041875229 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, 2001.
    • (2001) NIPS
    • Ng, A.1    Jordan, M.2    Weiss, Y.3
  • 21
    • 33845593841 scopus 로고    scopus 로고
    • A boundary-fragment-model for object detection
    • A. Opelt, A. Pinz, and A. Zisserman. A Boundary-Fragment-model for Object Detection. In ECCV, 2006.
    • (2006) ECCV
    • Opelt, A.1    Pinz, A.2    Zisserman, A.3
  • 22
    • 50649122703 scopus 로고    scopus 로고
    • Efficient mining of frequent and distinctive feature configurations
    • T. Quack, V. Ferrari, B. Leibe, and L. V. Gool. Efficient Mining of Frequent and Distinctive Feature Configurations. In ICCV, 2007.
    • (2007) ICCV
    • Quack, T.1    Ferrari, V.2    Leibe, B.3    Gool, L.V.4
  • 24
    • 33845596932 scopus 로고    scopus 로고
    • Using multiple segmentations to discover objects and their extent in image collections
    • B. Russell, A. Efros, J. Sivic, W. Freeman, and A. Zisserman. Using Multiple Segmentations to Discover Objects and their Extent in Image Collections. In CVPR, 2006.
    • (2006) CVPR
    • Russell, B.1    Efros, A.2    Sivic, J.3    Freeman, W.4    Zisserman, A.5
  • 25
    • 45349091203 scopus 로고    scopus 로고
    • Multi-scale categorical object recognition using contour fragments
    • J. Shotton, A. Blake, and R. Cipolla. Multi-Scale Categorical Object Recognition Using Contour Fragments. TPAMI, 30(7), 2008.
    • (2008) TPAMI , vol.30 , Issue.7
    • Shotton, J.1    Blake, A.2    Cipolla, R.3
  • 27
    • 33845579921 scopus 로고    scopus 로고
    • Extracting subimages of an unknown category from a set of images
    • S. Todorovic and N. Ahuja. Extracting Subimages of an Unknown Category from a Set of Images. In CVPR, 2006.
    • (2006) CVPR
    • Todorovic, S.1    Ahuja, N.2
  • 28
    • 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 ICCV, 2005.
    • (2005) ICCV
    • Winn, J.1    Jojic, N.2
  • 29
    • 50649111840 scopus 로고    scopus 로고
    • Spatial random partition for common visual pattern discovery
    • J. Yuan and Y. Wu. Spatial Random Partition for Common Visual Pattern Discovery. In ICCV, 2007.
    • (2007) ICCV
    • Yuan, J.1    Wu, Y.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.