메뉴 건너뛰기




Volumn , Issue , 2012, Pages 1338-1345

Exploiting web images for event recognition in consumer videos: A multiple source domain adaptation approach

Author keywords

[No Author keywords available]

Indexed keywords

ALTERNATING OPTIMIZATIONS; COMPUTER VISION APPLICATIONS; CONSUMER VIDEOS; DECISION FUNCTIONS; DECISION VALUE; DOMAIN ADAPTATION; EVENT RECOGNITION; KEY-FRAMES; MULTIPLE SOURCE; PERFORMANCE GAIN; REAL-WORLD DATASETS; REGULARIZER; SIFT FEATURE; SPACETIME; SUPPORT VECTOR REGRESSION (SVR); SVM CLASSIFIERS; WEB IMAGES;

EID: 84866710696     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2012.6247819     Document Type: Conference Paper
Times cited : (217)

References (30)
  • 1
    • 84856675275 scopus 로고    scopus 로고
    • Tabula rasa: Model transfer for object category detection
    • 2
    • Y. Aytar and A. Zisserman. Tabula rasa: Model transfer for object category detection. In ICCV, 2011. 2
    • (2011) ICCV
    • Aytar, Y.1    Zisserman, A.2
  • 3
    • 77949852900 scopus 로고    scopus 로고
    • Domain adaptation problems: A dasvm classification technique and a circular validation strategy
    • 2, 5
    • L. Bruzzone and M. Marconcini. Domain adaptation problems: a dasvm classification technique and a circular validation strategy. TPAMI, 32(5):770-787, 2010. 2, 5
    • (2010) TPAMI , vol.32 , Issue.5 , pp. 770-787
    • Bruzzone, L.1    Marconcini, M.2
  • 4
    • 10044235999 scopus 로고    scopus 로고
    • Libsvm: A library for support vector machines
    • 4, 5
    • C.-C. Chang and C.-J. Lin. Libsvm: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm, 2001. 4, 5
    • (2001) Software
    • Chang, C.-C.1    Lin, C.-J.2
  • 5
    • 84866681311 scopus 로고    scopus 로고
    • Multi-source domain adaptation and its application to early detectionof fatigue
    • 2, 3, 5
    • R. Chattopadhyay, J. Ye, S. Panchanathan, W. Fan, and I. Davidson. Multi-source domain adaptation and its application to early detectionof fatigue. In KDD, 2007. 2, 3, 5
    • (2007) KDD
    • Chattopadhyay, R.1    Ye, J.2    Panchanathan, S.3    Fan, W.4    Davidson, I.5
  • 6
    • 74049158146 scopus 로고    scopus 로고
    • Nuswide: A real-world web image database from national university of singapore
    • 5
    • T.-S. Chua, J. Tang, R. Hong, H. Li, Z. Luo, and Y.-T. Zheng. Nuswide: A real-world web image database from national university of singapore. In CIVR, 2009. 5
    • (2009) CIVR
    • Chua, T.-S.1    Tang, J.2    Hong, R.3    Li, H.4    Luo, Z.5    Zheng, Y.-T.6
  • 7
    • 84860513476 scopus 로고    scopus 로고
    • Frustratingly easy domain adaptation
    • 2, 3
    • H. Daumé III. Frustratingly easy domain adaptation. In ACL, 2007. 2, 3
    • (2007) ACL
    • Daumé III, H.1
  • 8
    • 84929624829 scopus 로고    scopus 로고
    • Boosting for transfer learning with multiple auxiliary domains
    • 2, 3, 5
    • G. Doretto and Y. Yao. Boosting for transfer learning with multiple auxiliary domains. In CVPR, 2010. 2, 3, 5
    • (2010) CVPR
    • Doretto, G.1    Yao, Y.2
  • 9
    • 79953064532 scopus 로고    scopus 로고
    • Domain adaptation from multiple sources via auxiliary classifiers
    • 2, 3,4, 5
    • L. Duan, I. W. Tsang, D. Xu, and T.-S. Chua. Domain adaptation from multiple sources via auxiliary classifiers. In ICML, 2009. 2, 3,4, 5
    • (2009) ICML
    • Duan, L.1    Tsang, I.W.2    Xu, D.3    Chua, T.-S.4
  • 10
    • 70450185098 scopus 로고    scopus 로고
    • Domain transfer svm for video concept detection
    • 2, 3
    • L. Duan, I. W. Tsang, D. Xu, and S. J. Maybank. Domain transfer svm for video concept detection. In CVPR, 2009. 2, 3
    • (2009) CVPR
    • Duan, L.1    Tsang, I.W.2    Xu, D.3    Maybank, S.J.4
  • 11
    • 77956003629 scopus 로고    scopus 로고
    • Visual event recognition in videos by learning from web data
    • 1, 2, 3, 5, 7
    • L. Duan, D. Xu, I. W. Tsang, and J. Luo. Visual event recognition in videos by learning from web data. In CVPR, 2010. 1, 2, 3, 5, 7
    • (2010) CVPR
    • Duan, L.1    Xu, D.2    Tsang, I.W.3    Luo, J.4
  • 12
    • 84863396387 scopus 로고    scopus 로고
    • Domain adaptation for object recognition: An unsupervised approach
    • 2, 3
    • R. Gopalan, R. Li, and R. Chellappa. Domain adaptation for object recognition: An unsupervised approach. In ICCV, 2011. 2, 3
    • (2011) ICCV
    • Gopalan, R.1    Li, R.2    Chellappa, R.3
  • 14
    • 79958737093 scopus 로고    scopus 로고
    • Object, scene and actions: Combining multiple features for human action recognition
    • 1
    • N. Ikizler-Cinbis and S. Sclaroff. Object, scene and actions: Combining multiple features for human action recognition. In ECCV, 2010. 1
    • (2010) ECCV
    • Ikizler-Cinbis, N.1    Sclaroff, S.2
  • 15
    • 79959766559 scopus 로고    scopus 로고
    • Consumer video understanding: A benchmark database and an evaluation ofhuman and machine performance
    • 1, 6
    • Y.-G. Jiang, G. Ye, S.-F. Chang, D. Ellis, and A. C. Loui. Consumer video understanding: A benchmark database and an evaluation ofhuman and machine performance. In ICMR, 2011. 1, 6
    • (2011) ICMR
    • Jiang, Y.-G.1    Ye, G.2    Chang, S.-F.3    Ellis, D.4    Loui, A.C.5
  • 16
    • 77955993558 scopus 로고    scopus 로고
    • Learning a hierarchy of discriminative space-time neighborhood features for human action recognition
    • 1
    • A. Kovashka and K. Grauman. Learning a hierarchy of discriminative space-time neighborhood features for human action recognition. In CVPR, 2010. 1
    • (2010) CVPR
    • Kovashka, A.1    Grauman, K.2
  • 17
    • 80052895155 scopus 로고    scopus 로고
    • What you saw is not what youget: Domain adaptation using asymmetric kernel transforms
    • 2
    • B. Kulis, K. Saenko, and T. Darrell. What you saw is not what youget: Domain adaptation using asymmetric kernel transforms. In CVPR, 2011. 2
    • (2011) CVPR
    • Kulis, B.1    Saenko, K.2    Darrell, T.3
  • 18
    • 79951678228 scopus 로고    scopus 로고
    • Recognizing realistic actions from videos
    • 1, 2, 3
    • J. Liu, J. Luo, and M. Shah. Recognizing realistic actions from videos. In CVPR, 2009. 1, 2, 3
    • (2009) CVPR
    • Liu, J.1    Luo, J.2    Shah, M.3
  • 19
    • 79953043001 scopus 로고    scopus 로고
    • Textual query of personal photos facilitated by large-scale web data
    • 6
    • Y. Liu, D. Xu, I.W. Tsang, and J. Luo. Textual query of personal photos facilitated by large-scale web data. T-PAMI, 33(5):1022-1036,2011. 6
    • (2011) T-PAMI , vol.33 , Issue.5 , pp. 1022-1036
    • Liu, Y.1    Xu, D.2    Tsang, I.W.3    Luo, J.4
  • 21
    • 3042535216 scopus 로고    scopus 로고
    • Distinctive image features from scale-invariant keypoints
    • 3, 5, 6
    • D. G. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60(2):91-110, 2004. 3, 5, 6
    • (2004) IJCV , vol.60 , Issue.2 , pp. 91-110
    • Lowe, D.G.1
  • 22
    • 77956031473 scopus 로고    scopus 로고
    • A survey on transfer learning
    • 2
    • S. J. Pan and Q. Yang. A survey on transfer learning. T-KDE, 22(10):1345-1359, 2010. 2
    • (2010) T-KDE , vol.22 , Issue.10 , pp. 1345-1359
    • Pan, S.J.1    Yang, Q.2
  • 23
    • 80052901068 scopus 로고    scopus 로고
    • Towards cross-category knowledge propagation for learning visualconcepts
    • 2
    • G.-J. Qi, C. Aggarwal, Y. Rui, Q. Tian, S. Chang, and T. Huang. Towards cross-category knowledge propagation for learning visualconcepts. In CVPR, 2011. 2
    • (2011) CVPR
    • Qi, G.-J.1    Aggarwal, C.2    Rui, Y.3    Tian, Q.4    Chang, S.5    Huang, T.6
  • 25
    • 78149301639 scopus 로고    scopus 로고
    • Adapting visual category models to new domains
    • 2
    • K. Saenko, B. Kulis, M. Fritz, and T. Darrell. Adapting visual category models to new domains. In ECCV, 2010. 2
    • (2010) ECCV
    • Saenko, K.1    Kulis, B.2    Fritz, M.3    Darrell, T.4
  • 26
    • 70049090801 scopus 로고    scopus 로고
    • An empiricalanalysis of domain adaptation algorithms for genomic sequence analysis
    • 2, 3, 5
    • G. Schweikert, C. Widmer, B. Schölkopf, and G. Rätsch. An empiricalanalysis of domain adaptation algorithms for genomic sequence analysis. In NIPS, 2009. 2, 3, 5
    • (2009) NIPS
    • Schweikert, G.1    Widmer, C.2    Schölkopf, B.3    Rätsch, G.4
  • 27
    • 4043137356 scopus 로고    scopus 로고
    • A tutorial on support vector regression
    • 4
    • A. J. Smola and B. Schölkopf. A tutorial on support vector regression. Statistics and Computing, 14(3):199-222, 2004. 4
    • (2004) Statistics and Computing , vol.14 , Issue.3 , pp. 199-222
    • Smola, A.J.1    Schölkopf, B.2
  • 28
    • 77956005674 scopus 로고    scopus 로고
    • Safety in numbers: Learning categories from few examples with multi model knowledge transfer
    • 2
    • T. Tommasi, F. Orabona, and B. Caputo. Safety in numbers: Learning categories from few examples with multi model knowledge transfer. In CVPR, 2010. 2
    • (2010) CVPR
    • Tommasi, T.1    Orabona, F.2    Caputo, B.3
  • 29
    • 80052877143 scopus 로고    scopus 로고
    • Action recognition by dense trajectories
    • 1, 3, 4, 6
    • H. Wang, A. Kläser, C. Schmid, and C.-L. Liu. Action recognition by dense trajectories. In CVPR, 2011. 1, 3, 4, 6
    • (2011) CVPR
    • Wang, H.1    Kläser, A.2    Schmid, C.3    Liu, C.-L.4
  • 30
    • 57549111074 scopus 로고    scopus 로고
    • Cross-domain video concept detection using adaptive svms
    • 2, 3
    • J. Yang, R. Yan, and A. G. Hauptmann. Cross-domain video concept detection using adaptive svms. In ACM MM, 2007. 2, 3
    • (2007) ACM MM
    • Yang, J.1    Yan, R.2    Hauptmann, A.G.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.