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Volumn 250, Issue , 2017, Pages 16-36

Discovering visual concept structure with sparse and incomplete tags

Author keywords

Data clustering; Incomplete tags; Missing tag completion; Random forest; Sparse tags; Tag correlation; Tag hierarchy; Visual semantic structure

Indexed keywords

ARTIFICIAL INTELLIGENCE; BENCHMARKING; CLUSTER ANALYSIS; CLUSTERING ALGORITHMS; DECISION TREES; RANDOM FORESTS;

EID: 85020674450     PISSN: 00043702     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artint.2017.05.002     Document Type: Article
Times cited : (19)

References (73)
  • 2
    • 77950369345 scopus 로고    scopus 로고
    • Data clustering: 50 years beyond k-means
    • Jain, A.K., Data clustering: 50 years beyond k-means. Pattern Recognit. Lett. 31:8 (2010), 651–666.
    • (2010) Pattern Recognit. Lett. , vol.31 , Issue.8 , pp. 651-666
    • Jain, A.K.1
  • 9
    • 3042673775 scopus 로고    scopus 로고
    • Linear dimensionality reduction via a heteroscedastic extension of LDA: the Chernoff criterion
    • Duin, R., Loog, M., Linear dimensionality reduction via a heteroscedastic extension of LDA: the Chernoff criterion. IEEE Trans. Pattern Anal. Mach. Intell. 26:6 (2004), 732–739.
    • (2004) IEEE Trans. Pattern Anal. Mach. Intell. , vol.26 , Issue.6 , pp. 732-739
    • Duin, R.1    Loog, M.2
  • 20
    • 10044285992 scopus 로고    scopus 로고
    • Canonical correlation analysis: an overview with application to learning methods
    • Hardoon, D.R., Szedmak, S., Shawe-Taylor, J., Canonical correlation analysis: an overview with application to learning methods. Neural Comput. 16:12 (2004), 2639–2664.
    • (2004) Neural Comput. , vol.16 , Issue.12 , pp. 2639-2664
    • Hardoon, D.R.1    Szedmak, S.2    Shawe-Taylor, J.3
  • 21
    • 84867097097 scopus 로고    scopus 로고
    • Learning the relative importance of objects from tagged images for retrieval and cross-modal search
    • Hwang, S.J., Grauman, K., Learning the relative importance of objects from tagged images for retrieval and cross-modal search. Int. J. Comput. Vis. 100:2 (2012), 134–153.
    • (2012) Int. J. Comput. Vis. , vol.100 , Issue.2 , pp. 134-153
    • Hwang, S.J.1    Grauman, K.2
  • 22
    • 84894905366 scopus 로고    scopus 로고
    • A multi-view embedding space for modeling internet images, tags, and their semantics
    • Gong, Y., Ke, Q., Isard, M., Lazebnik, S., A multi-view embedding space for modeling internet images, tags, and their semantics. Int. J. Comput. Vis. 106:2 (2014), 210–233.
    • (2014) Int. J. Comput. Vis. , vol.106 , Issue.2 , pp. 210-233
    • Gong, Y.1    Ke, Q.2    Isard, M.3    Lazebnik, S.4
  • 29
    • 84899651693 scopus 로고    scopus 로고
    • Classification in the presence of label noise: a survey
    • Frénay, B., Verleysen, M., Classification in the presence of label noise: a survey. IEEE Trans. Neural Netw. Learn. Syst. 25:5 (2014), 845–869.
    • (2014) IEEE Trans. Neural Netw. Learn. Syst. , vol.25 , Issue.5 , pp. 845-869
    • Frénay, B.1    Verleysen, M.2
  • 33
    • 71049116435 scopus 로고    scopus 로고
    • Exact matrix completion via convex optimization
    • Candès, E.J., Recht, B., Exact matrix completion via convex optimization. Found. Comput. Math. 9:6 (2009), 717–772.
    • (2009) Found. Comput. Math. , vol.9 , Issue.6 , pp. 717-772
    • Candès, E.J.1    Recht, B.2
  • 34
    • 0012686456 scopus 로고    scopus 로고
    • WordNet
    • Wiley Online Library
    • Fellbaum, C., WordNet. 1998, Wiley Online Library.
    • (1998)
    • Fellbaum, C.1
  • 42
    • 79953187637 scopus 로고    scopus 로고
    • Discriminative models for multi-class object layout
    • Desai, C., Ramanan, D., Fowlkes, C.C., Discriminative models for multi-class object layout. Int. J. Comput. Vis. 95:1 (2011), 1–12.
    • (2011) Int. J. Comput. Vis. , vol.95 , Issue.1 , pp. 1-12
    • Desai, C.1    Ramanan, D.2    Fowlkes, C.C.3
  • 45
    • 84859414659 scopus 로고    scopus 로고
    • Decision forests: a unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning
    • ® Comp. Graph. Vis. 7:2–3 (2012), 81–227.
    • (2012) ® Comp. Graph. Vis. , vol.7 , Issue.2-3 , pp. 81-227
    • Criminisi, A.1    Shotton, J.2    Konukoglu, E.3
  • 49
    • 0035478854 scopus 로고    scopus 로고
    • Random forests
    • Breiman, L., Random forests. Mach. Learn. 45:1 (2001), 5–32.
    • (2001) Mach. Learn. , vol.45 , Issue.1 , pp. 5-32
    • Breiman, L.1
  • 50
    • 33645573203 scopus 로고    scopus 로고
    • Unsupervised learning with random forest predictors
    • Shi, T., Horvath, S., Unsupervised learning with random forest predictors. J. Comput. Graph. Stat. 15:1 (2006), 118–138.
    • (2006) J. Comput. Graph. Stat. , vol.15 , Issue.1 , pp. 118-138
    • Shi, T.1    Horvath, S.2
  • 51
    • 0003802343 scopus 로고
    • Classification and Regression Trees
    • Chapman & Hall/CRC
    • Breiman, L., Friedman, J., Stone, C., Olshen, R., Classification and Regression Trees. 1984, Chapman & Hall/CRC.
    • (1984)
    • Breiman, L.1    Friedman, J.2    Stone, C.3    Olshen, R.4
  • 55
    • 84926662675 scopus 로고
    • Nearest neighbor pattern classification
    • Cover, T.M., Hart, P.E., Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13:1 (1967), 21–27.
    • (1967) IEEE Trans. Inf. Theory , vol.13 , Issue.1 , pp. 21-27
    • Cover, T.M.1    Hart, P.E.2
  • 56
    • 61749090884 scopus 로고    scopus 로고
    • Distance metric learning for large margin nearest neighbor classification
    • Weinberger, K.Q., Saul, L.K., Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn. Res. 10 (2009), 207–244.
    • (2009) J. Mach. Learn. Res. , vol.10 , pp. 207-244
    • Weinberger, K.Q.1    Saul, L.K.2
  • 66
    • 33847172327 scopus 로고    scopus 로고
    • Clustering by passing messages between data points
    • Frey, B.J., Dueck, D., Clustering by passing messages between data points. Science 315:5814 (2007), 972–976.
    • (2007) Science , vol.315 , Issue.5814 , pp. 972-976
    • Frey, B.J.1    Dueck, D.2
  • 69
    • 84950632109 scopus 로고
    • Objective criteria for the evaluation of clustering methods
    • Rand, W.M., Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 66:336 (1971), 846–850.
    • (1971) J. Am. Stat. Assoc. , vol.66 , Issue.336 , pp. 846-850
    • Rand, W.M.1
  • 70
    • 4344611435 scopus 로고    scopus 로고
    • Properties of the Hubert–Arable adjusted Rand index
    • Steinley, D., Properties of the Hubert–Arable adjusted Rand index. Psychol. Methods, 9(3), 2004, 386.
    • (2004) Psychol. Methods , vol.9 , Issue.3 , pp. 386
    • Steinley, D.1
  • 71
    • 49649154957 scopus 로고
    • The use of hierarchic clustering in information retrieval
    • Jardine, N., van Rijsbergen, C.J., The use of hierarchic clustering in information retrieval. Inf. Storage Retr., 1971, 217–240.
    • (1971) Inf. Storage Retr. , pp. 217-240
    • Jardine, N.1    van Rijsbergen, C.J.2
  • 72
    • 78649420560 scopus 로고    scopus 로고
    • Information theoretic measures for clusterings comparison: variants, properties, normalization and correction for chance
    • Vinh, N.X., Epps, J., Bailey, J., Information theoretic measures for clusterings comparison: variants, properties, normalization and correction for chance. J. Mach. Learn. Res. 11 (2010), 2837–2854.
    • (2010) J. Mach. Learn. Res. , vol.11 , pp. 2837-2854
    • Vinh, N.X.1    Epps, J.2    Bailey, J.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.