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Volumn 2015-January, Issue , 2015, Pages 1378-1386

Submodboxes: Near-optimal search for a set of diverse object proposals

Author keywords

[No Author keywords available]

Indexed keywords

HEURISTIC METHODS; TREES (MATHEMATICS);

EID: 84965108927     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (10)

References (41)
  • 1
    • 84866688216 scopus 로고    scopus 로고
    • Measuring the objectness of image windows
    • Nov, 7
    • B. Alexe, T. Deselaers, and V. Ferrari. Measuring the objectness of image windows. PAMI, 34(11):2189-2202, Nov. 2012. 7
    • (2012) PAMI , vol.34 , Issue.11 , pp. 2189-2202
    • Alexe, B.1    Deselaers, T.2    Ferrari, V.3
  • 3
    • 80051773712 scopus 로고    scopus 로고
    • Branch and bound strategies for non-maximal suppression in object detection
    • 1, 3, 5
    • M. Blaschko. Branch and bound strategies for non-maximal suppression in object detection. In EMM-CVPR, pages 385-398, 2011. 1, 3, 5
    • (2011) EMM-CVPR , pp. 385-398
    • Blaschko, M.1
  • 4
    • 56749161572 scopus 로고    scopus 로고
    • Learning to localize objects with structured output regression
    • 2
    • M. B. Blaschko and C. H. Lampert. Learning to localize objects with structured output regression. In ECCV, 2008. 2
    • (2008) ECCV
    • Blaschko, M.B.1    Lampert, C.H.2
  • 5
    • 84871947114 scopus 로고    scopus 로고
    • A tight (1/2) linear-time approximation to unconstrained submodular maximization
    • 5
    • N. Buchbinder, M. Feldman, J. Naor, and R. Schwartz. A tight (1/2) linear-time approximation to unconstrained submodular maximization. In FOCS, 2012. 5
    • (2012) FOCS
    • Buchbinder, N.1    Feldman, M.2    Naor, J.3    Schwartz, R.4
  • 7
    • 77956008665 scopus 로고    scopus 로고
    • Constrained parametric min-cuts for automatic object segmentation
    • 1, 2, 7
    • J. Carreira and C. Sminchisescu. Constrained parametric min-cuts for automatic object segmentation. In CVPR, 2010. 1, 2, 7
    • (2010) CVPR
    • Carreira, J.1    Sminchisescu, C.2
  • 8
    • 84911456915 scopus 로고    scopus 로고
    • Bing: Binarized normed gradients for objectness estimation at 300fps
    • 1
    • M.-M. Cheng, Z. Zhang, W.-Y. Lin, and P. Torr. Bing: binarized normed gradients for objectness estimation at 300fps. In CVPR, 2014. 1
    • (2014) CVPR
    • Cheng, M.-M.1    Zhang, Z.2    Lin, W.-Y.3    Torr, P.4
  • 9
    • 33645146449 scopus 로고    scopus 로고
    • Histograms of oriented gradients for human detection
    • 1, 3
    • N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR, 2005. 1, 3
    • (2005) CVPR
    • Dalal, N.1    Triggs, B.2
  • 10
    • 79959728283 scopus 로고    scopus 로고
    • Localizing objects while learning their appearance
    • 1
    • T. Deselaers, B. Alexe, and V. Ferrari. Localizing objects while learning their appearance. In ECCV, 2010. 1
    • (2010) ECCV
    • Deselaers, T.1    Alexe, B.2    Ferrari, V.3
  • 11
  • 12
    • 0000988422 scopus 로고
    • Branch-and-bound methods: A survey
    • 2
    • E. L. Lawler and D. E. Wood. Branch-and-bound methods: A survey. Operations Research, 14(4):699-719, 1966. 2
    • (1966) Operations Research , vol.14 , Issue.4 , pp. 699-719
    • Lawler, E.L.1    Wood, D.E.2
  • 15
    • 46749125782 scopus 로고    scopus 로고
    • Maximizing non-monotone submodular functions
    • 5
    • U. Feige, V. Mirrokni, and J. Vondrák. Maximizing non-monotone submodular functions. In FOCS, 2007. 5
    • (2007) FOCS
    • Feige, U.1    Mirrokni, V.2    Vondrák, J.3
  • 16
    • 77955422240 scopus 로고    scopus 로고
    • Object detection with discriminatively trained part based models
    • 1, 3
    • P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained part based models. PAMI, 32(9):1627-1645, 2010. 1, 3
    • (2010) PAMI , vol.32 , Issue.9 , pp. 1627-1645
    • Felzenszwalb, P.F.1    Girshick, R.B.2    McAllester, D.3    Ramanan, D.4
  • 17
    • 84911400494 scopus 로고    scopus 로고
    • Rich feature hierarchies for accurate object detection and semantic segmentation
    • 1, 3
    • R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR, 2014. 1, 3
    • (2014) CVPR
    • Girshick, R.1    Donahue, J.2    Darrell, T.3    Malik, J.4
  • 18
    • 84959186479 scopus 로고    scopus 로고
    • An active search strategy for efficient object detection
    • 3
    • A. Gonzalez-Garcia, A. Vezhnevets, and V. Ferrari. An active search strategy for efficient object detection. In CVPR, 2015. 3
    • (2015) CVPR
    • Gonzalez-Garcia, A.1    Vezhnevets, A.2    Ferrari, V.3
  • 19
    • 84928278589 scopus 로고    scopus 로고
    • Spatial pyramid pooling in deep convolutional networks for visual recognition
    • 1, 3
    • K. He, X. Zhang, S. Ren, and J. Sun. Spatial pyramid pooling in deep convolutional networks for visual recognition. In ECCV, 2014. 1, 3
    • (2014) ECCV
    • He, K.1    Zhang, X.2    Ren, S.3    Sun, J.4
  • 20
    • 85081111493 scopus 로고    scopus 로고
    • How good are detection proposals, really?
    • 3, 7
    • J. Hosang, R. Benenson, and B. Schiele. How good are detection proposals, really? In BMVC, 2014. 3, 7
    • (2014) BMVC
    • Hosang, J.1    Benenson, R.2    Schiele, B.3
  • 21
    • 69549111057 scopus 로고    scopus 로고
    • Cutting-plane training of structural svms
    • 2
    • T. Joachims, T. Finley, and C.-N. Yu. Cutting-plane training of structural svms. Machine Learning, 77(1):27-59, 2009. 2
    • Machine Learning , vol.77 , Issue.1 , pp. 27-592009
    • Joachims, T.1    Finley, T.2    Yu, C.-N.3
  • 23
    • 84959200010 scopus 로고    scopus 로고
    • Learning to propose objects
    • 7
    • P. Krahenbuhl and V. Koltun. Learning to propose objects. In CVPR, 2015. 7
    • (2015) CVPR
    • Krahenbuhl, P.1    Koltun, V.2
  • 25
    • 41549146576 scopus 로고    scopus 로고
    • Near-optimal sensor placements in Gaussian processes: Theory, efficient algorithms and empirical studies
    • 3
    • A. Krause, A. Singh, and C. Guestrin. Near-optimal sensor placements in gaussian processes: Theory, efficient algorithms and empirical studies. J. Mach. Learn. Res., 9:235-284, 2008. 3
    • (2008) J. Mach. Learn. Res. , vol.9 , pp. 235-284
    • Krause, A.1    Singh, A.2    Guestrin, C.3
  • 26
    • 70350621774 scopus 로고    scopus 로고
    • Efficient subwindow search: A branch and bound framework for object localization
    • 1, 2, 3, 4, 5
    • C. H. Lampert, M. B. Blaschko, and T. Hofmann. Efficient subwindow search: A branch and bound framework for object localization. TPMAI, 31(12):2129-2142, 2009. 1, 2, 3, 4, 5
    • (2009) TPMAI , vol.31 , Issue.12 , pp. 2129-2142
    • Lampert, C.H.1    Blaschko, M.B.2    Hofmann, T.3
  • 27
    • 84859070008 scopus 로고    scopus 로고
    • A class of submodular functions for document summarization
    • 3
    • H. Lin and J. Bilmes. A class of submodular functions for document summarization. In ACL, 2011. 3
    • (2011) ACL
    • Lin, H.1    Bilmes, J.2
  • 29
    • 77951148076 scopus 로고
    • Accelerated greedy algorithms for maximizing submodular set functions
    • 2, 6
    • M. Minoux. Accelerated greedy algorithms for maximizing submodular set functions. Optimization Techniques, pages 234-243, 1978. 2, 6
    • (1978) Optimization Techniques , pp. 234-243
    • Minoux, M.1
  • 30
    • 0000095809 scopus 로고
    • An analysis of approximations for maximizing submodular set functions
    • 2, 3
    • G. Nemhauser, L. Wolsey, and M. Fisher. An analysis of approximations for maximizing submodular set functions. Mathematical Programming, 14(1):265-294, 1978. 2, 3
    • (1978) Mathematical Programming , vol.14 , Issue.1 , pp. 265-294
    • Nemhauser, G.1    Wolsey, L.2    Fisher, M.3
  • 31
    • 84937919648 scopus 로고    scopus 로고
    • Submodular meets structured: Finding diverse subsets in exponentially-large structured item sets
    • 3
    • A. Prasad, S. Jegelka, and D. Batra. Submodular meets structured: Finding diverse subsets in exponentially-large structured item sets. In NIPS, 2014. 3
    • (2014) NIPS
    • Prasad, A.1    Jegelka, S.2    Batra, D.3
  • 32
    • 84960980241 scopus 로고    scopus 로고
    • Faster r-cnn: Towards real-time object detection with region proposal networks
    • 1
    • S. Ren, K. He, R. Girshick, and J. Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. In NIPS, 2015. 1
    • (2015) NIPS
    • Ren, S.1    He, K.2    Girshick, R.3    Sun, J.4
  • 33
    • 84897551647 scopus 로고    scopus 로고
    • Learning policies for contextual submodular prediction
    • 4
    • S. Ross, J. Zhou, Y. Yue, D. Dey, and J. A. Bagnell. Learning policies for contextual submodular prediction. In ICML, 2013. 4
    • (2013) ICML
    • Ross, S.1    Zhou, J.2    Yue, Y.3    Dey, D.4    Bagnell, J.A.5
  • 34
    • 85083951635 scopus 로고    scopus 로고
    • Overfeat: Integrated recognition, localization and detection using convolutional networks
    • 1
    • P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun. Overfeat: Integrated recognition, localization and detection using convolutional networks. In ICLR, 2014. 1
    • (2014) ICLR
    • Sermanet, P.1    Eigen, D.2    Zhang, X.3    Mathieu, M.4    Fergus, R.5    LeCun, Y.6
  • 35
    • 85047019092 scopus 로고    scopus 로고
    • An online algorithm for maximizing submodular functions
    • 4
    • M. Streeter and D. Golovin. An online algorithm for maximizing submodular functions. In NIPS, 2008. 4
    • (2008) NIPS
    • Streeter, M.1    Golovin, D.2
  • 36
    • 84961630841 scopus 로고    scopus 로고
    • Scalable, high-quality object detection
    • 1, 3
    • C. Szegedy, S. Reed, and D. Erhan. Scalable, high-quality object detection. In CVPR, 2014. 1, 3
    • (2014) CVPR
    • Szegedy, C.1    Reed, S.2    Erhan, D.3
  • 37
    • 84898989329 scopus 로고    scopus 로고
    • Deep neural networks for object detection
    • 1
    • C. Szegedy, A. Toshev, and D. Erhan. Deep neural networks for object detection. In NIPS, 2013. 1
    • (2013) NIPS
    • Szegedy, C.1    Toshev, A.2    Erhan, D.3
  • 40
    • 2142812371 scopus 로고    scopus 로고
    • Robust real-time face detection
    • May, 1, 3
    • P. Viola and M. J. Jones. Robust real-time face detection. Int. J. Comput. Vision, 57(2):137-154, May 2004. 1, 3
    • (2004) Int. J. Comput. Vision , vol.57 , Issue.2 , pp. 137-154
    • Viola, P.1    Jones, M.J.2
  • 41
    • 84952018709 scopus 로고    scopus 로고
    • Edge boxes: Locating object proposals from edges
    • 1, 2, 3, 4, 5, 7
    • C. Zitnick and P. Dollar. Edge boxes: Locating object proposals from edges. In ECCV, 2014. 1, 2, 3, 4, 5, 7
    • (2014) ECCV
    • Zitnick, C.1    Dollar, P.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.