메뉴 건너뛰기




Volumn 2015 International Conference on Computer Vision, ICCV 2015, Issue , 2015, Pages 91-99

Local convolutional features with unsupervised training for image retrieval

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; CONTENT BASED RETRIEVAL; CONVOLUTION; NETWORK ARCHITECTURE; STEREO IMAGE PROCESSING; STEREO VISION;

EID: 84973878915     PISSN: 15505499     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2015.19     Document Type: Conference Paper
Times cited : (170)

References (47)
  • 4
    • 80052882128 scopus 로고    scopus 로고
    • Object recognition with hierarchical kernel descriptors
    • L. Bo, K. Lai, X. Ren, and D. Fox. Object recognition with hierarchical kernel descriptors. In CVPR, 2011. 5
    • (2011) CVPR , vol.5
    • Bo, L.1    Lai, K.2    Ren, X.3    Fox, D.4
  • 5
    • 85162018819 scopus 로고    scopus 로고
    • Kernel descriptors for visual recognition
    • L. Bo, X. Ren, and D. Fox. Kernel descriptors for visual recognition. In NIPS, 2010. 2, 5
    • (2010) NIPS , vol.2 , pp. 5
    • Bo, L.1    Ren, X.2    Fox, D.3
  • 7
    • 78649324041 scopus 로고    scopus 로고
    • Discriminative learning of local image descriptors
    • M. Brown, G. Hua, and S. Winder. Discriminative learning of local image descriptors. PAMI, 2011. 2
    • (2011) PAMI , vol.2
    • Brown, M.1    Hua, G.2    Winder, S.3
  • 8
    • 79952521625 scopus 로고    scopus 로고
    • BRIEF: Binary robust independent elementary features
    • M. Calonder, V. Lepetit, C. Strecha, and P. Fua. BRIEF: Binary robust independent elementary features. In ECCV, 2010. 2
    • (2010) ECCV , vol.2
    • Calonder, M.1    Lepetit, V.2    Strecha, C.3    Fua, P.4
  • 11
    • 85067215717 scopus 로고    scopus 로고
    • Discriminative unsupervised feature learning with convolutional neural networks
    • A. Dosovitskiy, J. T. Springenberg, M. Riedmiller, and T. Brox. Discriminative unsupervised feature learning with convolutional neural networks. NIPS, 2014. 2, 4
    • (2014) NIPS , vol.2 , pp. 4
    • Dosovitskiy, A.1    Springenberg, J.T.2    Riedmiller, M.3    Brox, T.4
  • 13
    • 84911400494 scopus 로고    scopus 로고
    • Rich feature hierarchies for accurate object detection and semantic segmentation
    • 1, 2
    • R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR, 2014. 1, 2
    • (2014) CVPR
    • Girshick, R.1    Donahue, J.2    Darrell, T.3    Malik, J.4
  • 14
    • 84938217896 scopus 로고    scopus 로고
    • Multi-scale orderless pooling of deep convolutional activation features
    • Y. Gong, L. Wang, R. Guo, and S. Lazebnik. Multi-scale orderless pooling of deep convolutional activation features. In ECCV, 2014. 3, 8
    • (2014) ECCV , vol.3 , pp. 8
    • Gong, Y.1    Wang, L.2    Guo, R.3    Lazebnik, S.4
  • 15
    • 84877623414 scopus 로고    scopus 로고
    • Negative evidences and cooccurrences in image retrieval: The benefit of PCA and whitening
    • H. Jégou and O. Chum. Negative evidences and cooccurrences in image retrieval: The benefit of PCA and whitening. In ECCV, 2012. 8
    • (2012) ECCV , vol.8
    • Jégou, H.1    Chum, O.2
  • 16
    • 70449560133 scopus 로고    scopus 로고
    • Hamming embedding and weak geometric consistency for large scale image search
    • H. Jegou, M. Douze, and C. Schmid. Hamming embedding and weak geometric consistency for large scale image search. In ECCV. 2008. 2
    • (2008) ECCV , vol.2
    • Jegou, H.1    Douze, M.2    Schmid, C.3
  • 17
    • 84875881757 scopus 로고    scopus 로고
    • Product quantization for nearest neighbor search
    • H. Jegou, M. Douze, and C. Schmid. Product quantization for nearest neighbor search. PAMI, 2011. 6, 8
    • (2011) PAMI , vol.6 , pp. 8
    • Jegou, H.1    Douze, M.2    Schmid, C.3
  • 18
    • 77956004473 scopus 로고    scopus 로고
    • Aggregating local descriptors into a compact image representation
    • H. Jégou, M. Douze, C. Schmid, and P. Pérez. Aggregating local descriptors into a compact image representation. In CVPR, 2010. 3
    • (2010) CVPR , vol.3
    • Jégou, H.1    Douze, M.2    Schmid, C.3    Pérez, P.4
  • 22
    • 84876231242 scopus 로고    scopus 로고
    • ImageNet classification with deep convolutional neural networks
    • 1, 3, 4
    • A. Krizhevsky, I. Sutskever, and G. Hinton. ImageNet classification with deep convolutional neural networks. In NIPS, 2012. 1, 3, 4
    • (2012) NIPS
    • Krizhevsky, A.1    Sutskever, I.2    Hinton, G.3
  • 24
    • 84856654969 scopus 로고    scopus 로고
    • Location recognition using prioritized feature matching
    • Y. Li, N. Snavely, and D. P. Huttenlocher. Location recognition using prioritized feature matching. In ECCV. 2010. 6
    • (2010) ECCV , vol.6
    • Li, Y.1    Snavely, N.2    Huttenlocher, D.P.3
  • 25
    • 84959224983 scopus 로고    scopus 로고
    • Do Convnets learn correspondances
    • 1, 2, 3
    • J. Long, N. Zhang, and T. Darrell. Do Convnets learn correspondances In NIPS, 2014. 1, 2, 3
    • (2014) NIPS
    • Long, J.1    Zhang, N.2    Darrell, T.3
  • 26
    • 3042535216 scopus 로고    scopus 로고
    • Distinctive image features from scale-invariant keypoints
    • 1, 2, 3
    • D. G. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 2004. 1, 2, 3
    • (2004) IJCV
    • Lowe, D.G.1
  • 28
    • 9644260534 scopus 로고    scopus 로고
    • Scale & affine invariant interest point detectors
    • K. Mikolajczyk and C. Schmid. Scale & affine invariant interest point detectors. IJCV, 2004. 3
    • (2004) IJCV , vol.3
    • Mikolajczyk, K.1    Schmid, C.2
  • 29
    • 27644547620 scopus 로고    scopus 로고
    • A performance evaluation of local descriptors
    • 2, 5
    • K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors. PAMI, 2005. 2, 5
    • (2005) PAMI
    • Mikolajczyk, K.1    Schmid, C.2
  • 31
    • 84911449395 scopus 로고    scopus 로고
    • Learning and transferring mid-level image representations using convolutional neural networks
    • 1, 2
    • M. Oquab, L. Bottou, I. Laptev, and J. Sivic. Learning and transferring mid-level image representations using convolutional neural networks. In CVPR, 2014. 1, 2
    • (2014) CVPR
    • Oquab, M.1    Bottou, L.2    Laptev, I.3    Sivic, J.4
  • 32
    • 70450179951 scopus 로고    scopus 로고
    • Efficient representation of local geometry for large scale object retrieval
    • M. Perdoch, O. Chum, and J. Matas. Efficient representation of local geometry for large scale object retrieval. In CVPR, 2009. 8
    • (2009) CVPR , vol.8
    • Perdoch, M.1    Chum, O.2    Matas, J.3
  • 33
    • 34948815101 scopus 로고    scopus 로고
    • Fisher kernels on visual vocabularies for image categorization
    • F. Perronnin and C. Dance. Fisher kernels on visual vocabularies for image categorization. In CVPR, 2007. 2
    • (2007) CVPR , vol.2
    • Perronnin, F.1    Dance, C.2
  • 34
    • 77956008923 scopus 로고    scopus 로고
    • Large-scale image categorization with explicit data embedding
    • 2, 4
    • F. Perronnin, J. Sánchez, and Y. Liu. Large-scale image categorization with explicit data embedding. In CVPR, 2010. 2, 4
    • (2010) CVPR
    • Perronnin, F.1    Sánchez, J.2    Liu, Y.3
  • 35
    • 34948903793 scopus 로고    scopus 로고
    • Object retrieval with large vocabularies and fast spatial matching
    • J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman. Object retrieval with large vocabularies and fast spatial matching. In CVPR, 2007. 6
    • (2007) CVPR , vol.6
    • Philbin, J.1    Chum, O.2    Isard, M.3    Sivic, J.4    Zisserman, A.5
  • 36
    • 51949105132 scopus 로고    scopus 로고
    • Lost in quantization: Improving particular object retrieval in large scale image databases
    • J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman. Lost in quantization: Improving particular object retrieval in large scale image databases. In CVPR, 2008. 8
    • (2008) CVPR , vol.8
    • Philbin, J.1    Chum, O.2    Isard, M.3    Sivic, J.4    Zisserman, A.5
  • 37
    • 80052894806 scopus 로고    scopus 로고
    • Descriptor learning for efficient retrieval
    • J. Philbin, M. Isard, J. Sivic, and A. Zisserman. Descriptor learning for efficient retrieval. In ECCV. 2010. 2
    • (2010) ECCV , vol.2
    • Philbin, J.1    Isard, M.2    Sivic, J.3    Zisserman, A.4
  • 40
    • 84911454787 scopus 로고    scopus 로고
    • Learning local feature descriptors using convex optimisation
    • K. Simonyan, A. Vedaldi, and A. Zisserman. Learning local feature descriptors using convex optimisation. PAMI, 2014. 2
    • (2014) PAMI , vol.2
    • Simonyan, K.1    Vedaldi, A.2    Zisserman, A.3
  • 41
    • 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 ICCV, 2003. 1
    • (2003) ICCV , vol.1
    • Sivic, J.1    Zisserman, A.2
  • 42
    • 77949875753 scopus 로고    scopus 로고
    • Daisy: An efficient dense descriptor applied to wide-baseline stereo
    • E. Tola, V. Lepetit, and P. Fua. Daisy: An efficient dense descriptor applied to wide-baseline stereo. PAMI, 2010. 2
    • (2010) PAMI , vol.2
    • Tola, E.1    Lepetit, V.2    Fua, P.3
  • 44
    • 84856194352 scopus 로고    scopus 로고
    • Efficient additive kernels via explicit feature maps
    • 2, 4
    • A. Vedaldi and A. Zisserman. Efficient additive kernels via explicit feature maps. TPAMI, 2012. 2, 4
    • (2012) TPAMI
    • Vedaldi, A.1    Zisserman, A.2
  • 45
    • 84863054049 scopus 로고    scopus 로고
    • Local intensity order pattern for feature description
    • Z. Wang, B. Fan, and F. Wu. Local intensity order pattern for feature description. In ICCV, 2011. 2
    • (2011) ICCV , vol.2
    • Wang, Z.1    Fan, B.2    Wu, F.3
  • 46
    • 70450208928 scopus 로고    scopus 로고
    • Picking the best daisy
    • 2, 3, 6
    • S. Winder, G. Hua, and M. Brown. Picking the best daisy. In CVPR, 2009. 2, 3, 6
    • (2009) CVPR
    • Winder, S.1    Hua, G.2    Brown, M.3
  • 47
    • 84937508363 scopus 로고    scopus 로고
    • How transferable are features in deep neural networks
    • J. Yosinski, J. Clune, Y. Bengio, and H. Lipson. How transferable are features in deep neural networks In NIPS, 2014. 2
    • (2014) NIPS , vol.2
    • Yosinski, J.1    Clune, J.2    Bengio, Y.3    Lipson, H.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.