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Volumn 07-12-June-2015, Issue , 2015, Pages 3022-3031

An active search strategy for efficient object class detection

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; DECISION TREES;

EID: 84959186479     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2015.7298921     Document Type: Conference Paper
Times cited : (62)

References (57)
  • 3
  • 4
    • 80053442030 scopus 로고    scopus 로고
    • Learning attentional policies for tracking and recognition in video with deep networks
    • L. Bazzani, N. de Freitas, H. Larochelle, V. Murino, and J. Ting. Learning attentional policies for tracking and recognition in video with deep networks. In ICML, 2011
    • (2011) ICML
    • Bazzani, L.1    De Freitas, N.2    Larochelle, H.3    Murino, V.4    Ting, J.5
  • 5
    • 0035478854 scopus 로고    scopus 로고
    • Random forests
    • L. Breiman. Random forests. Machine Learning, 45(1):5-32, 2001
    • (2001) Machine Learning , vol.45 , Issue.1 , pp. 5-32
    • Breiman, L.1
  • 6
    • 70450161413 scopus 로고    scopus 로고
    • Optimal scanning for faster object detection
    • N. J. Butko and J. R. Movellan. Optimal scanning for faster object detection. In CVPR, 2009
    • (2009) CVPR
    • Butko, N.J.1    Movellan, J.R.2
  • 7
    • 77956006912 scopus 로고    scopus 로고
    • Exploiting hierarchical context on a large database of object categories
    • M. Choi, J. Lim, A. Torralba, and A. Willsky. Exploiting hierarchical context on a large database of object categories. In CVPR, 2010
    • (2010) CVPR
    • Choi, M.1    Lim, J.2    Torralba, A.3    Willsky, A.4
  • 8
    • 84898774288 scopus 로고    scopus 로고
    • Segmentation driven object detection with fisher vectors
    • R. Cinbis, J. Verbeek, and C. Schmid. Segmentation driven object detection with fisher vectors. In ICCV, 2013
    • (2013) ICCV
    • Cinbis, R.1    Verbeek, J.2    Schmid, C.3
  • 10
    • 33645146449 scopus 로고    scopus 로고
    • Histogram of Oriented Gradients for human detection
    • N. Dalal and B. Triggs. Histogram of Oriented Gradients for human detection. In CVPR, 2005
    • (2005) CVPR
    • Dalal, N.1    Triggs, B.2
  • 11
    • 33645146449 scopus 로고    scopus 로고
    • Histogram of oriented gradients for human detection
    • N. Dalal and B. Triggs. Histogram of Oriented Gradients for Human Detection. In CVPR, volume 2, pages 886-893, 2005
    • (2005) CVPR , vol.2 , pp. 886-893
    • Dalal, N.1    Triggs, B.2
  • 12
    • 84867478719 scopus 로고    scopus 로고
    • Learning where to attend with deep architectures for image tracking
    • M. Denil, L. Bazzani, H. Larochelle, and N. de Freitas. Learning where to attend with deep architectures for image tracking. Neural Computation, 24(8):2151-184, 2012
    • (2012) Neural Computation , vol.24 , Issue.8 , pp. 2151-2184
    • Denil, M.1    Bazzani, L.2    Larochelle, H.3    De Freitas, N.4
  • 13
    • 77953180995 scopus 로고    scopus 로고
    • Discriminative models for multi-class object layout
    • C. Desai, D. Ramanan, and C. Folkess. Discriminative models for multi-class object layout. In ICCV, 2009
    • (2009) ICCV
    • Desai, C.1    Ramanan, D.2    Folkess, C.3
  • 14
    • 70450161428 scopus 로고    scopus 로고
    • An empirical study of context in object detection
    • S. K. Divvala, D. Hoiem, J. H. Hays, A. A. Efros, and M. Hebert. An empirical study of context in object detection. In CVPR, pages 1271-1278, 2009
    • (2009) CVPR , pp. 1271-1278
    • Divvala, S.K.1    Hoiem, D.2    Hays, J.H.3    Efros, A.A.4    Hebert, M.5
  • 17
    • 80052874104 scopus 로고    scopus 로고
    • Real time head pose estimation with random regression forests
    • IEEE
    • G. Fanelli, J. Gall, and L. Van Gool. Real time head pose estimation with random regression forests. In CVPR, pages 617-624. IEEE, 2011
    • (2011) CVPR , pp. 617-624
    • Fanelli, G.1    Gall, J.2    Van Gool, L.3
  • 18
    • 77955999401 scopus 로고    scopus 로고
    • Cascade object detection with deformable part models
    • P. Felzenszwalb, R. Girshick, and D. McAllester. Cascade Object Detection with Deformable Part Models. In CVPR, 2010
    • (2010) CVPR
    • Felzenszwalb, P.1    Girshick, R.2    McAllester, D.3
  • 20
    • 70450201402 scopus 로고    scopus 로고
    • Class-specific hough forests for object detection
    • J. Gall and V. Lempitsky. Class-specific hough forests for object detection. In CVPR, 2009
    • (2009) CVPR
    • Gall, J.1    Lempitsky, V.2
  • 21
    • 51949110976 scopus 로고    scopus 로고
    • Object categorization using co-occurrence, location and appearance
    • C. Galleguillos, A. Rabinovich, and S. Belongie. Object categorization using co-occurrence, location and appearance. In CVPR, 2008
    • (2008) CVPR
    • Galleguillos, C.1    Rabinovich, A.2    Belongie, S.3
  • 22
    • 84911400494 scopus 로고    scopus 로고
    • Rich feature hierarchies for accurate object detection and semantic segmentation
    • R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR, 2014
    • (2014) CVPR
    • Girshick, R.1    Donahue, J.2    Darrell, T.3    Malik, J.4
  • 23
    • 84856653054 scopus 로고    scopus 로고
    • Efficient regression of general-activity human poses from depth images
    • IEEE
    • R. Girshick, J. Shotton, P. Kohli, A. Criminisi, and A. Fitzgibbon. Efficient regression of general-activity human poses from depth images. In ICCV, pages 415-422. IEEE, 2011
    • (2011) ICCV , pp. 415-422
    • Girshick, R.1    Shotton, J.2    Kohli, P.3    Criminisi, A.4    Fitzgibbon, A.5
  • 24
    • 80052874105 scopus 로고    scopus 로고
    • Iterative quantization: A procrustean approach to learning binary codes
    • Y. Gong and S. Lazebnik. Iterative quantization: A procrustean approach to learning binary codes. In CVPR, 2011
    • (2011) CVPR
    • Gong, Y.1    Lazebnik, S.2
  • 25
    • 0002944464 scopus 로고
    • On the estimation of entropy
    • P. Hall and S. C. Morton. On the estimation of entropy. AISM, 45(1):69-88, 1993
    • (1993) AISM , vol.45 , Issue.1 , pp. 69-88
    • Hall, P.1    Morton, S.C.2
  • 26
    • 77953202990 scopus 로고    scopus 로고
    • Combining efficient object localization and image classification
    • H. Harzallah, F. Jurie, and C. Schmid. Combining efficient object localization and image classification. In ICCV, 2009
    • (2009) ICCV
    • Harzallah, H.1    Jurie, F.2    Schmid, C.3
  • 27
    • 84928278589 scopus 로고    scopus 로고
    • Spatial pyramid pooling in deep convolutional networks for visual recognition
    • K. He, X. Zhang, S. Ren, and J. Sun. Spatial pyramid pooling in deep convolutional networks for visual recognition. In ECCV, 2014
    • (2014) ECCV
    • He, K.1    Zhang, X.2    Ren, S.3    Sun, J.4
  • 28
    • 70449582001 scopus 로고    scopus 로고
    • Learning spatial context: Using stuff to find things
    • G. Heitz and D. Koller. Learning spatial context: Using stuff to find things. In ECCV, 2008
    • (2008) ECCV
    • Heitz, G.1    Koller, D.2
  • 30
    • 84876231242 scopus 로고    scopus 로고
    • Imagenet classification with deep convolutional neural networks
    • A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012
    • (2012) NIPS
    • Krizhevsky, A.1    Sutskever, I.2    Hinton, G.E.3
  • 31
    • 51949099868 scopus 로고    scopus 로고
    • Beyond sliding windows: Object localization by efficient subwindow search
    • C. H. Lampert, M. B. Blaschko, and T. Hofmann. Beyond sliding windows: Object localization by efficient subwindow search. In CVPR, 2008
    • (2008) CVPR
    • Lampert, C.H.1    Blaschko, M.B.2    Hofmann, T.3
  • 32
    • 85162061663 scopus 로고    scopus 로고
    • Learning to combine foveal glimpses with a third-order Boltzmann machine
    • H. Larochelle and G. E. Hinton. Learning to combine foveal glimpses with a third-order Boltzmann machine. In NIPS, 2010
    • (2010) NIPS
    • Larochelle, H.1    Hinton, G.E.2
  • 33
    • 84898422177 scopus 로고    scopus 로고
    • Branch&rank: Non-linear object detection
    • A. Lehmann, P. V. Gehler, and L. J. Van Gool. Branch&rank: Non-linear object detection. In BMVC, volume 2, page 1, 2011
    • (2011) BMVC , vol.2 , pp. 1
    • Lehmann, A.1    Gehler, P.V.2    Van Gool, L.J.3
  • 34
    • 84863018717 scopus 로고    scopus 로고
    • Extracting adaptive contextual cues from unlabeled regions
    • IEEE
    • C. Li, D. Parikh, and T. Chen. Extracting adaptive contextual cues from unlabeled regions. In ICCV, pages 511-518. IEEE, 2011
    • (2011) ICCV , pp. 511-518
    • Li, C.1    Parikh, D.2    Chen, T.3
  • 35
    • 84863411575 scopus 로고    scopus 로고
    • Ensemble of exemplar-svms for object detection and beyond
    • T. Malisiewicz, A. Gupta, and A. Efros. Ensemble of exemplar-svms for object detection and beyond. In ICCV, 2011
    • (2011) ICCV
    • Malisiewicz, T.1    Gupta, A.2    Efros, A.3
  • 36
    • 84898781017 scopus 로고    scopus 로고
    • Prime object proposals with randomized prim's algorithm
    • S. Manen, M. Guillaumin, and L. Van Gool. Prime object proposals with randomized prim's algorithm. In ICCV, 2013
    • (2013) ICCV
    • Manen, S.1    Guillaumin, M.2    Van Gool, L.3
  • 37
    • 84937959846 scopus 로고    scopus 로고
    • Recurrent models of visual attention
    • V. Mnih, N. Heess, and A. Graves. Recurrent models of visual attention. In NIPS, pages 2204-2212, 2014
    • (2014) NIPS , pp. 2204-2212
    • Mnih, V.1    Heess, N.2    Graves, A.3
  • 39
    • 85044773291 scopus 로고    scopus 로고
    • Fast discriminative visual codebook using randomized clustering forests
    • F. Moosman, B. Triggs, and F. Jurie. Fast discriminative visual codebook using randomized clustering forests. In NIPS, 2006
    • (2006) NIPS
    • Moosman, F.1    Triggs, B.2    Jurie, F.3
  • 40
    • 84911444024 scopus 로고    scopus 로고
    • The role of context for object detection and semantic segmentation in the wild
    • IEEE
    • R. Mottaghi, X. Chen, X. Liu, N.-G. Cho, S.-W. Lee, S. Fidler, R. Urtasun, and A. Yuille. The role of context for object detection and semantic segmentation in the wild. In CVPR, pages 891-898. IEEE, 2014
    • (2014) CVPR , pp. 891-898
    • Mottaghi, R.1    Chen, X.2    Liu, X.3    Cho, N.-G.4    Lee, S.-W.5    Fidler, S.6    Urtasun, R.7    Yuille, A.8
  • 41
    • 24644462948 scopus 로고    scopus 로고
    • Using the forest to see the trees: A graphical model relating features, objects, and scenes
    • K. Murphy, A. Torralba, andW. T. Freeman. Using the forest to see the trees: A graphical model relating features, objects, and scenes. In NIPS, 2003
    • (2003) NIPS
    • Murphy, K.1    Torralba, A.2    Freeman, A.T.3
  • 42
    • 84875633536 scopus 로고    scopus 로고
    • Are you using the right approximate nearest neighbor algorithm
    • S. O'Hara and B. A. Draper. Are you using the right approximate nearest neighbor algorithm? In wacv, 2013
    • (2013) Wacv
    • O'Hara, S.1    Draper, B.A.2
  • 43
    • 80052908750 scopus 로고    scopus 로고
    • A coarse-to-fine approach for fast deformable object detection
    • M. Pedersoli, A. Vedaldi, and J. Gonzales. A coarse-to-fine approach for fast deformable object detection. In CVPR, 2011
    • (2011) CVPR
    • Pedersoli, M.1    Vedaldi, A.2    Gonzales, J.3
  • 45
    • 84907341129 scopus 로고    scopus 로고
    • Boosting algorithms for detector cascade learning
    • M. Saberian and N. Vasconcelos. Boosting algorithms for detector cascade learning. JMLR, 15(1):2569-2605, 2014
    • (2014) JMLR , vol.15 , Issue.1 , pp. 2569-2605
    • Saberian, M.1    Vasconcelos, N.2
  • 46
    • 77956064399 scopus 로고    scopus 로고
    • Active testing for face detection and localization
    • R. Sznitman and B. Jedynak. Active testing for face detection and localization. IEEE Trans. on PAMI, 2010
    • (2010) IEEE Trans. on PAMI
    • Sznitman, R.1    Jedynak, B.2
  • 47
    • 84937843152 scopus 로고    scopus 로고
    • Learning generative models with visual attention
    • Y. Tang, N. Srivastava, and R. Salakhutdinov. Learning generative models with visual attention. In NIPS, pages 1808-1816, 2014
    • (2014) NIPS , pp. 1808-1816
    • Tang, Y.1    Srivastava, N.2    Salakhutdinov, R.3
  • 48
    • 0037500818 scopus 로고    scopus 로고
    • Contextual priming for object detection
    • A. Torralba. Contextual priming for object detection. IJCV, 53(2):153-167, 2003
    • (2003) IJCV , vol.53 , Issue.2 , pp. 153-167
    • Torralba, A.1
  • 50
    • 77955426203 scopus 로고    scopus 로고
    • Evaluating color descriptors for object and scene recognition
    • K. E. A. Van De Sande, T. Gevers, and C. G. M. Snoek. Evaluating color descriptors for object and scene recognition. IEEE Trans. on PAMI, 32(9):1582-1596, 2010
    • (2010) IEEE Trans. on PAMI , vol.32 , Issue.9 , pp. 1582-1596
    • Sande De Van, A.K.E.1    Gevers, T.2    Snoek, C.G.M.3
  • 52
    • 0035680116 scopus 로고    scopus 로고
    • Rapid object detection using a boosted cascade of simple features
    • P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. In CVPR, pages 511-518, 2001
    • (2001) CVPR , pp. 511-518
    • Viola, P.1    Jones, M.2
  • 53
    • 84898769710 scopus 로고    scopus 로고
    • Regionlets for generic object detection
    • IEEE
    • X. Wang, M. Yang, S. Zhu, and Y. Lin. Regionlets for generic object detection. In ICCV, pages 17-24. IEEE, 2013
    • (2013) ICCV , pp. 17-24
    • Wang, X.1    Yang, M.2    Zhu, S.3    Lin, Y.4
  • 54
    • 84898809063 scopus 로고    scopus 로고
    • Learning near-optimal cost-sensitive decision policy for object detection
    • T. Wu and S. Zhu. Learning near-optimal cost-sensitive decision policy for object detection. In ICCV, pages 753-760, 2013
    • (2013) ICCV , pp. 753-760
    • Wu, T.1    Zhu, S.2
  • 55
    • 77955988947 scopus 로고    scopus 로고
    • SUN database: Large-scale scene recognition from Abbey to Zoo
    • J. Xiao, J. Hays, K. Ehinger, A. Oliva, and A. Torralba. SUN database: Large-scale scene recognition from Abbey to Zoo. In CVPR, 2010
    • (2010) CVPR
    • Xiao, J.1    Hays, J.2    Ehinger, K.3    Oliva, A.4    Torralba, A.5


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