Olga Zoidi
Skip Abstract Section
Abstract
The expansion of the Internet over the last decade and the proliferation of online social communities, such as Facebook, Google+, and Twitter, as well as multimedia sharing sites, such as YouTube, Flickr, and Picasa, has led to a vast increase of available information to the user. In the case of multimedia data, such as images and videos, fast querying and processing of the available information requires the annotation of the multimedia data with semantic descriptors, that is, labels. However, only a small proportion of the available data are labeled. The rest should undergo an annotation-labeling process. The necessity for the creation of automatic annotation algorithms gave birth to label propagation and semi-supervised learning. In this study, basic concepts in graph-based label propagation methods are discussed. Methods for proper graph construction based on the structure of the available data and label inference methods for spreading label information from a few labeled data to a larger set of unlabeled data are reviewed. Applications of label propagation algorithms in digital media, as well as evaluation metrics for measuring their performance, are presented.
- M. Alonso and E. J. Finn. 1967. Fundamental University Physics. Addison-Wesley.Google Scholar
- A. Amir, M. Berg, S. F. Chang, W. Hsu, G. Iyengar, C. Y. Lin, M. Naphade, A. Natsev, C. Neti, H. Nock, et al. 2003. IBM research TRECVID-2003 video retrieval system. In NIST TRECVID-2003.Google Scholar
- A. Argyriou, M. Herbster, and M. Pontil. 2005. Combining graph laplacians for semi-supervised learning. In Advances in Neural Information Processing Systems 18. MIT Press, 67--74.Google Scholar
- V. Badrinarayanan, F. Galasso, and R. Cipolla. 2010. Label propagation in video sequences. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’10). 3265--3272.Google Scholar
- S. Baluja, R. Seth, D. Sivakumar, Y. Jing, J. Yagnik, S. Kumar, D. Ravichandran, and M. Aly. 2008. Video suggestion and discovery for YouTube: Taking random walks through the view graph. In Proceedings of the 17th International Conference on World Wide Web. 895--904. Google ScholarDigital Library
- B. K. Bao, B. Ni, Y. Mu, and S. Yan. 2011. Efficient region-aware large graph construction towards scalable multi-label propagation. Pattern Recognition 44, 3 (2011), 598--606. Google ScholarDigital Library
- R. Barrett, M. Berry, T. F. Chan, J. Demmel, J. Donato, J. Dongarra, V. Eijkhout, R. Pozo, C. Romine, and H. Van der Vorst. 1994. Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods, 2nd Edition. SIAM, Philadelphia, PA.Google Scholar
- M. Belkin, I. Matveeva, and P. Niyogi. 2004. Regularization and semi-supervised learning on large graphs. In COLT. Springer, 624--638.Google Scholar
- M. Belkin and P. Niyogi. 2003. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15, 6 (2003), 1373--1396. Google ScholarDigital Library
- M. Belkin, P. Niyogi, and V. Sindhwani. 2006. Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research 7 (2006), 2399--2434. Google ScholarDigital Library
- Y. Bengio, O. Delalleau, and N. Le Roux. 2006. Label propagation and quadratic criterion. In Semi-Supervised Learning. MIT Press, 193--216.Google Scholar
- C. Berge. 1989. Hypergraphs: Combinatorics of Finite Sets. Vol. 45. North Holland.Google Scholar
- K. Beyer, J. Goldstein, R. Ramakrishnan, and U. Shaft. 1999. When is nearest neighbor meaningful? In Database Theory ICDT’99. Springer, 217--235. Google ScholarDigital Library
- H. J. Bierens. 1987. Kernel estimators of regression functions. In Advances in Econometrics: Fifth World Congress of the Econometric Society, Vol. 1. 99--144.Google ScholarCross Ref
- M. Bilenko, S. Basu, and R. J. Mooney. 2004. Integrating constraints and metric learning in semi-supervised clustering. In Proceedings of the 21st International Conference on Machine Learning. ACM, 11. Google ScholarDigital Library
- A. Blum and S. Chawla. 2001. Learning from labeled and unlabeled data using graph mincuts. In Proceedings of the 18th International Conference on Machine Learning (ICML’01). 19--26. Google ScholarDigital Library
- A. Blum, J. Lafferty, M. R. Rwebangira, and R. Reddy. 2004. Semi-supervised learning using randomized mincuts. In Proceedings of the 21st International Conference on Machine Learning (ICML’04). ACM, 13. Google ScholarDigital Library
- L. E. Blume. 1993. The statistical mechanics of strategic interaction. Games and Economic Behavior 5 (1993), 387--424.Google ScholarCross Ref
- I. Borg and P. J. F. Groenen. 2005. Modern Multidimensional Scaling. Springer.Google Scholar
- G. J. Brostow, J. Fauqueur, and R. Cipolla. 2009. Semantic object classes in video: A high-definition ground truth database. Pattern Recognition Letters 30, 2 (2009), 88--97. Google ScholarDigital Library
- I. Budvytis, V. Badrinarayanan, and R. Cipolla. 2010. Label propagation in complex video sequences using semi-supervised learning. In BMVC, Vol. 2257. 2258--2259.Google Scholar
- A. Y. C. Chen and J. J. Corso. 2010. Propagating multi-class pixel labels throughout video frames. In Proceedings of the Western New York Image Processing Workshop (WNYIPW’10). 14--17.Google Scholar
- A. Y. C. Chen and J. J. Corso. 2011. Temporally consistent multi-class video-object segmentation with the Video Graph-Shifts algorithm. In Proceedings of the IEEE Workshop on Applications of Computer Vision (WACV’11). 614--621. Google ScholarDigital Library
- G. Chen, J. Zhang, F. Wang, C. Zhang, and Y. Gao. 2009. Efficient multi-label classification with hypergraph regularization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 1658--1665.Google Scholar
- X. Chen, Y. Mu, S. Yan, and T. S. Chua. 2010. Efficient large-scale image annotation by probabilistic collaborative multi-label propagation. In Proceedings of the International Conference on Multimedia (MM’10). ACM, 35--44. Google ScholarDigital Library
- H. Cheng, Z. Liu, and J. Yang. 2009. Sparsity induced similarity measure for label propagation. In Proceedings of the IEEE 12th International Conference on Computer Vision. 317--324.Google Scholar
- D. Coppi, S. Calderara, and R. Cucchiara. 2011. People appearance tracing in video by spectral graph transduction. In Proceedings of the IEEE International Conference on Computer Vision Workshops. 920--927.Google Scholar
- A. Corduneanu and T. Jaakkola. 2004. Distributed information regularization on graphs. Neural Information Proccessing Systems (NIPS).Google Scholar
- S. I. Daitch, J. A. Kelner, and D. A. Spielman. 2009. Fitting a graph to vector data. In Proceedings of the 26th Annual International Conference on Machine Learning (ICML’09). ACM, 201--208. Google ScholarDigital Library
- J. V. Davis, B. Kulis, P. Jain, S. Sra, and I. S. Dhillon. 2007. Information-theoretic metric learning. In Proceedings of the 24th International Conference on Machine Learning (ICML’07). ACM, 209--216. Google ScholarDigital Library
- P. S. Dhillon, P. P. Talukdar, and K. Crammer. 2010. Learning better data representation using inference-driven metric learning. In Proceedings of the ACL 2010 Conference Short Papers (ACLShort’10). 377--381. Google ScholarDigital Library
- C. Ding, H. D. Simon, R. Jin, and T. Li. 2007. A learning framework using Green’s function and kernel regularization with application to recommender system. In Proceedings of the 13th ACM SIGKDD international Conference on Knowledge Discovery and Data Mining. ACM, 260--269. Google ScholarDigital Library
- L. Ding and A. Yilmaz. 2008. Image segmentation as learning on hypergraphs. In Proceedings of the 7th International Conference on Machine Learning and Applications. IEEE, 247--252. Google ScholarDigital Library
- P. Domingos and M. Richardson. 2001. Mining the network value of customers. In Proc. of KDD. 57--66. Google ScholarDigital Library
- A. Elisseeff and J. Weston. 2001. A kernel method for multi-labelled classification. Advances in Neural Information Processing Systems 14 (2001), 681--687.Google ScholarDigital Library
- G. Ellison. 1993. Learning, local interaction, and coordination. Econometrica 61, 5 (1993), 1047--1071.Google ScholarCross Ref
- P. F. Evangelista, M. J. Embrechts, and B. K. Szymanski. 2006. Taming the curse of dimensionality in kernels and novelty detection. In Applied Soft Computing Technologies: The Challenge of Complexity. Springer, 425--438.Google Scholar
- J. Goldberger, S. Roweis, G. Hinton, and R. Salakhutdinov. 2004. Neighbourhood components analysis. In Advances in Neural Information Processing Systems 17. MIT Press, 513--520.Google Scholar
- J. Goldenberg, B. Libai, and E. Muller. 2001. Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing Letters 12, 3 (2001), 211--223.Google ScholarCross Ref
- L. Gomez-Chova, G. Camps-Valls, J. Munoz-Mari, and J. Calpe. 2008. Semisupervised image classification with laplacian support vector machines. IEEE Geoscience and Remote Sensing Letters 5, 3 (July 2008), 336--340.Google ScholarCross Ref
- L. Grady. 2006. Random walks for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 11 (2006), 1768--1783. Google ScholarDigital Library
- L. Grady and G. Funka-Lea. 2004. Multi-label image segmentation for medical applications based on graph-theoretic electrical potentials. In Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis. Lecture Notes in Computer Science, Vol. 3117. Springer, Berlin, 230--245.Google Scholar
- M. Granovetter. 1978. Threshold models of collective behavior. Amer. J. Sociology 83, 6 (1978), 1420--1433.Google ScholarCross Ref
- S. Gregory. 2010. Finding overlapping communities in networks by label propagation. New Journal of Physics 12, 10 (2010), 103018.Google ScholarCross Ref
- M. Guillaumin, T. Mensink, J. Verbeek, and C. Schmid. 2009. TagProp: Discriminative metric learning in nearest neighbor models for image auto-annotation. In Proceedings of the IEEE 12th International Conference on Computer Vision. 309--316.Google Scholar
- L. Hagen and A. B. Kahng. 1992. New spectral methods for ratio cut partitioning and clustering. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 11, 9 (Sept. 1992), 1074--1085. Google ScholarDigital Library
- R. A. Heckemann, J. V. Hajnal, P. Aljabar, D. Rueckert, and A. Hammers. 2006. Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. NeuroImage 33, 1 (2006), 115--126.Google ScholarCross Ref
- S. C. H. Hoi, W. Liu, and S. F. Chang. 2008. Semi-supervised distance metric learning for collaborative image retrieval. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 1--7.Google Scholar
- M. E. Houle, V. Oria, S. Satoh, and J. Sun. 2013. Annotation propagation in image databases using similarity graphs. ACM Transactions on Multimedia Computer Communication Applications 10, 1 (2013), 7:1--7:21. Google ScholarDigital Library
- T. Hwang and R. Kuang. 2010. A heterogeneous label propagation algorithm for disease gene discovery. In SDM’10. 583--594.Google Scholar
- T. Jebara, J. Wang, and S. F. Chang. 2009. Graph construction and b-matching for semi-supervised learning. In Proceedings of the 26th Annual International Conference on Machine Learning. ACM, 441--448. Google ScholarDigital Library
- T. Joachims. 2003. Transductive learning via spectral graph partitioning. In Proceedings of the International Conference on Machine Learning (ICML’03). ACM, 290--297.Google Scholar
- T. Joachims, N. Cristianini, and J. Shawe-Taylor. 2001. Composite kernels for hypertext categorisation. In Proceedings of the International Conference on Machine Learning (ICML’01). Morgan Kaufmann, 250--257. Google ScholarDigital Library
- I. T. Jolliffe. 2002. Principal Component Analysis, 2nd Edition. Springer.Google Scholar
- T. Kato, H. Kashima, and M. Sugiyama. 2009. Robust label propagation on multiple networks. IEEE Transactions on Neural Networks 20, 1 (2009), 35--44. Google ScholarDigital Library
- D. Kempe, J. Kleinberg, and É. Tardos. 2003. Maximizing the spread of influence through a social network. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Google ScholarDigital Library
- D. Kempe, J. Kleinberg, and É. Tardos. 2005. Influential nodes in a diffusion model for social networks. In Proceedings of the 32nd International Conference on Automata, Languages and Programming (ICALP’05). 1127--1138. Google ScholarDigital Library
- R. I. Kondor and J. Lafferty. 2002. Diffusion kernels on graphs and other discrete structures. In Proceedings of the ICML. 315--322. Google ScholarDigital Library
- D. Kuettel, M. Guillaumin, and V. Ferrari. 2012. Segmentation propagation in imagenet. In Proceedings of the 12th European Conference on Computer Vision - Volume Part VII (ECCV’12). Springer-Verlag, 459--473. Google ScholarDigital Library
- W. Y. Lee, L. C. Hsieh, G. L. Wu, and W. Hsu. 2013. Graph-based semi-supervised learning with multi-modality propagation for large-scale image datasets. Journal of Visual Communication Image Representation 24, 3 (April 2013), 295--302. Google ScholarDigital Library
- S. Letovsky and S. Kasif. 2003. Predicting protein function from protein/protein interaction data: A probabilistic approach. Bioinformatics 19, 1 (2003), i197--i204.Google ScholarCross Ref
- C. Y. Lin, B. L. Tseng, M. Naphade, A. Natsev, and J. R. Smith. 2003c. VideoAL: A novel end-to-end MPEG-7 video automatic labeling system. In Proceedings of the IEEE International Conference on Image Processing, Vol. 3. III--53--6 vol.2.Google Scholar
- C. Y. Lin, B. L. Tseng, and J. R. Smith. 2003a. Video collaborative annotation forum: Establishing ground-truth labels on large multimedia datasets. In Proceedings of the TRECVID 2003 Workshop.Google Scholar
- C. Y. Lin, B. L. Tseng, and J. R. Smith. 2003b. VideoAnnEx: IBM MPEG-7 annotation tool for multimedia indexing and concept learning. In Proceedings of the IEEE International Conference on Multimedia and Expo.Google Scholar
- D. Liu, X. S. Hua, L. Yang, M. Wang, and H. J. Zhang. 2009. Tag ranking. In Proceedings of the 18th International Conference on World Wide Web (WWW’09). ACM, New York, NY, 351--360. Google ScholarDigital Library
- D. Liu, S. Yan, X. S. Hua, and H. J. Zhang. 2011. Image retagging using collaborative tag propagation. IEEE Transactions on Multimedia 13, 4 (2011), 702--712. Google ScholarDigital Library
- J. Liu, W. Lai, X. S. Hua, Y. Huang, and S. Li. 2007. Video search re-ranking via multi-graph propagation. In Proceedings of the 15th International Conference on Multimedia (MULTIMEDIA’07). 208--217. Google ScholarDigital Library
- J. Liu, M. Li, Q. Liu, H. Lu, and S. Ma. 2009. Image annotation via graph learning. Pattern Recognition 42, 2 (2009), 218--228. Google ScholarDigital Library
- J. Long, J. Yin, W. Zhao, and E. Zhu. 2008. Graph-based active learning based on label propagation. In Modeling Decisions for Artificial Intelligence. Lecture Notes in Computer Science, Vol. 5285. Springer, Berlin, 179--190. Google ScholarDigital Library
- H. Ma, H. Yang, M. R. Lyu, and I. King. 2008. Mining social networks using heat diffusion processes for marketing candidates selection. In CIKM. Google ScholarDigital Library
- M. Maier, U. Von Luxburg, and M. Hein. 2008. Influence of graph construction on graph-based clustering measures. In Neural Information Processing Systems. 1025--1032.Google Scholar
- S. Morris. 2000. Contagion. Review of Economic Studies 67 (2000).Google Scholar
- M. E. J. Newman. 2001. Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. Physical Review E 64, 1 (2001), 016132.Google ScholarCross Ref
- N. Nguyen and Y. Guo. 2008. Metric learning: A support vector approach. In Machine Learning and Knowledge Discovery in Databases. 125--136. Google ScholarDigital Library
- Z. Y. Niu, D. H. Ji, and C. L. Tan. 2005. Word sense disambiguation using label propagation based semi-supervised learning. In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics (ACL’05). 395--402. Google ScholarDigital Library
- T. Opsahl, F. Agneessens, and J. Skvoretz. 2010. Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks 32, 3 (2010), 245--251.Google ScholarCross Ref
- M. Opuszko and J. Ruhland. 2013. Impact of the network structure on the SIR model spreading phenomena in online networks. In Proceedings of the 8th International Multi-Conference on Computing in the Global Information Technology (ICCGI’13).Google Scholar
- M. Orbach and K. Crammer. 2012. Graph-based transduction with confidence. In Proceedings of the 2012 European Conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II (ECML PKDD’12). Springer-Verlag, Berlin, 323--338. Google ScholarDigital Library
- R. Pastor-Satorras and A. Vespignani. 2001. Epidemic dynamics and endemic states in complex networks. Physical Review E.Google Scholar
- T. P. Phi, T. Tuytelaars, and M. F. Moens. 2011. Naming people in news videos with label propagation. IEEE MultiMedia 18, 3 (March 2011), 44--55. Google ScholarDigital Library
- G. J. Qi, X. S. Hua, Y. Rui, J. Tang, T. Mei, and H. J. Zhang. 2007. Correlative multi-label video annotation. In Proceedings of the 15th International Conference on Multimedia. 17--26. Google ScholarDigital Library
- D. Rao and D. Ravichandran. 2009. Semi-supervised polarity lexicon induction. In Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics (EACL’09). 675--682. Google ScholarDigital Library
- R. Rao. 2002. Probabilistic Models of the Brain: Perception and Neural Function. MIT Press.Google Scholar
- J. A. Rodríguez. 2003. On the Laplacian spectrum and walk-regular hypergraphs. Linear and Multilinear Algebra 51, 3 (2003), 285--297.Google ScholarCross Ref
- E. Rogers. 1962. Diffusion of Innovations. Free Press, New York.Google Scholar
- S. T. Roweis and L. K. Saul. 2000. Nonlinear dimensionality reduction by locally linear embedding. Science 290, 5500 (2000), 2323--2326.Google ScholarCross Ref
- M. Rubinstein, C. Liu, and W. T. Freeman. 2012. Annotation propagation in large image databases via dense image correspondence. In Computer Vision ECCV 2012. Lecture Notes in Computer Science, Vol. 7574. Springer, Berlin, 85--99. Google ScholarDigital Library
- H. Rue and L. Held. 2005. Gaussian Markov Random Fields: Theory and Applications. Chapman & Hall. Google ScholarDigital Library
- O. Russakovsky, J. Deng, Z. Huang, A. C. Berg, and L. Fei-Fei. 2013. Detecting avocados to zucchinis: What have we done, and where are we going? In Proceedings of the International Conference on Computer Vision (ICCV’13). Google ScholarDigital Library
- V. Satuluri and S. Parthasarathy. 2009. Scalable graph clustering using stochastic flows: Applications to community discovery. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 737--746. Google ScholarDigital Library
- L. K. Saul and S. T. Roweis. 2003. Think globally, fit locally: Unsupervised learning of low dimensional manifolds. Journal of Machine Learning Research 4 (2003), 119--155. Google ScholarDigital Library
- T. Schelling. 1978. Micromotives and Macrobehavior. Norton.Google Scholar
- B. D. Shai, L. Tyler, and P. Dávid. 2008. Does unlabeled data provably help? Worst-case analysis of the sample complexity of semi-supervised learning. In Proceedings of the 21st Annual Conference on Learning Theory.Google Scholar
- M. S. Shang, Z. K. Zhang, T. Zhoub, and Y. C. Zhang. 2010. Collaborative filtering with diffusion-based similarity on tripartite graphs. Physica A 389, 6 (2010), 1259--1264.Google ScholarCross Ref
- B. Shao, D. Wang, T. Li, and M. Ogihara. 2009. Music recommendation based on acoustic features and user access patterns. IEEE Transactions on Audio, Speech, and Language Processing 17, 8 (2009), 1602--1611. Google ScholarDigital Library
- J. Shi and J. Malik. 2000. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 8 (Aug. 2000), 888--905. Google ScholarDigital Library
- X. Shi, B. Tseng, and L. Adamic. 2009. Information diffusion in computer science citation networks. In Proceedings of the International Conference on Weblogs and Social Media.Google Scholar
- V. Sindhwani and P. Niyogi. 2005. A co-regularized approach to semi-supervised learning with multiple views. In Proceedings of the ICML Workshop on Learning with Multiple Views.Google Scholar
- A. Singh, R. D. Nowak, and X. Zhu. 2008. Unlabeled data: Now it helps, now it doesn’t. In NIPS’08. 1513--1520.Google Scholar
- A. Smola and R. Kondor. 2003. Kernels and regularization on graphs. In Proceedings of the Conference on Learning Theory and Kernel Machines. 144--158.Google Scholar
- C. G. M. Snoek, M. Worring, and A. W. M. Smeulders. 2005. Early versus late fusion in semantic video analysis. In Proceedings of the 13th Annual ACM International Conference on Multimedia (MULTIMEDIA’05). 399--402. Google ScholarDigital Library
- M. Speriosu, N. Sudan, S. Upadhyay, and J. Baldridge. 2011. Twitter polarity classification with label propagation over lexical links and the follower graph. In Proceedings of the 1st Workshop on Unsupervised Learning in NLP (EMNLP’11). 53--63. Google ScholarDigital Library
- A. Subramanya and J. Bilmes. 2011. Semi-supervised learning with measure propagation. Journal of Machine Learning Research 12 (2011), 3311--3370. Google ScholarDigital Library
- L. Sun, S. Ji, and J. Ye. 2008. Hypergraph spectral learning for multi-label classification. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 668--676. Google ScholarDigital Library
- M. Szummer and T. Jaakkola. 2002. Partially labeled classification with Markov random walks. In Advances in Neural Information Processing Systems. MIT Press, 945--952.Google Scholar
- P. P. Talukdar. 2009. Topics in Graph Construction for Semi-Supervised Learning. Technical Report, University of Pennsylvania.Google Scholar
- P. P. Talukdar and K. Crammer. 2009. New regularized algorithms for transductive learning. In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II (ECML PKDD’09). Springer-Verlag, Berlin, 442--457. Google ScholarDigital Library
- J. Tang, R. Hong, S. Yan, T. S. Chua, G. J. Qi, and R. Jain. 2011. Image annotation by kNN-sparse graph-based label propagation over noisily tagged web images. ACM Transactions on Intelligent System Technology 2, 2 (2011), 14:1--14:15. Google ScholarDigital Library
- J. Tang, X. S. Hua, G.-J. Qi, T. Mei, and X. Wu. 2007. Anisotropic manifold ranking for video annotation. In Proceedings of the IEEE International Conference on Multimedia and Expo. 492--495.Google Scholar
- J. Tang, X.-S. Hua, G.-J. Qi, Y. Song, and X. Wu. 2008. Video annotation based on kernel linear neighborhood propagation. IEEE Transactions on Multimedia 10, 4 (June 2008), 620--628. Google ScholarDigital Library
- J. Tang, X.-S. Hua, M. Wang, Z. Gu, G.-J. Qi, and X. Wu. 2009. Correlative linear neighborhood propagation for video annotation. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 39, 2 (April 2009), 409--416. Google ScholarDigital Library
- J. Tang, M. Li, Z. Li, and C. Zhao. 2014. Tag ranking based on salient region graph propagation. Multimedia Systems (2014), 1--9.Google Scholar
- J. Tang, S. Yan, R. Hong, G. J. Qi, and T. S. Chua. 2009. Inferring semantic concepts from community-contributed images and noisy tags. In Proceedings of the 17th ACM international conference on Multimedia (MM’09). ACM, 223--232. Google ScholarDigital Library
- J. B. Tenenbaum, V. De Silva, and J. C. Langford. 2000. A global geometric framework for nonlinear dimensionality reduction. Science 290, 5500 (2000), 2319--2323.Google ScholarCross Ref
- L. Terveen and W. Hill. 2001. Beyond recommender systems: Helping people help each other. In HCI in the New Millennium, Jack Carroll, ed. Addison-Wesley.Google Scholar
- Z. Tian, T. Hwang, and R. Kuang. 2009. A hypergraph-based learning algorithm for classifying gene expression and array CGH data with prior knowledge. Bioinformatics 25, 21 (2009), 2831--2838. Google ScholarDigital Library
- H. Tong, J. He, M. Li, C. Zhang, and W. Y. Ma. 2005. Graph based multi-modality learning. In Proceedings of the 13th Annual ACM International Conference on Multimedia. ACM, 862--871. Google ScholarDigital Library
- I. W. Tsang and J. T. Kwok. 2006. Large-scale sparsified manifold regularization. In Advances in Neural Information Processing Systems (NIPS) 19.Google Scholar
- K. Tsuda. 2005. Propagating distributions on a hypergraph by dual information regularization. In Proceedings of the 22nd International Conference on Machine Learning. ACM, 920--927. Google ScholarDigital Library
- K. Tsuda, H. Shin, and B. Schölkopf. 2005. Fast protein classification with multiple networks. Bioinformatics 21, 2 (2005), 59--65. Google ScholarDigital Library
- S. Vijayanarasimhan and K. Grauman. 2012. Active frame selection for label propagation in videos. In Proceedings of the 12th European conference on Computer Vision - Volume Part V (ECCV’12). 496--509. Google ScholarDigital Library
- J. von Neumann and O. Morgenstern. 1944. Theory of Games and Economic Behavior. Princeton University Press.Google Scholar
- C. Wang, F. Jing, L. Zhang, and H. J. Zhang. 2006. Image annotation refinement using random walk with restarts. In Proceedings of the 14th Annual ACM International Conference on Multimedia (MULTIMEDIA’06). ACM, 647--650. Google ScholarDigital Library
- D. Wang, I. King, and K. S. Leung. 2011. “Like attracts like!”—A social recommendation framework through label propagation. In Proceedings of the SIGIR 2011 Workshop on Social Web Search and Mining: Content Analysis Under Crisis.Google Scholar
- F. Wang, X. Wang, and T. Li. 2007. Efficient label propagation for interactive image segmentation. In Proceedings of the 6th International Conference on Machine Learning and Applications (ICMLA’07). 136--141. Google ScholarDigital Library
- F. Wang, X. Wang, B. Shao, T. Li, and M. Ogihara. 2009. Tag integrated multi-label music style classification with hypergraph. In Proceedings of the 10th International Society for Music Information Retrieval. 363--368.Google Scholar
- F. Wang and C. Zhang. 2006. Label propagation through linear neighborhoods. In Proceedings of the 23rd International Conference on Machine Learning (ICML’06). ACM, 985--992. Google ScholarDigital Library
- J. Wang, F. Wang, C. Zhang, H. C. Shen, and L. Quan. 2009. Linear neighborhood propagation and its applications. IEEE Transactions on Pattern Analysis and Machine Intelligence 31, 9 (Sept. 2009), 1600--1615. Google ScholarDigital Library
- J. Wang, J. Wang, G. Zeng, Z. Tu, R. Gan, and S. Li. 2012. Scalable k-NN graph construction for visual descriptors. In CVPR.Google Scholar
- M. Wang, X. S. Hua, R. Hong, J. Tang, G.-J. Qi, and Y. Song. 2009a. Unified video annotation via multigraph learning. IEEE Transactions on Circuits and Systems for Video Technology 19, 5 (2009), 733--746. Google ScholarDigital Library
- M. Wang, X. S. Hua, J. Tang, and R. Hong. 2009b. Beyond distance measurement: Constructing neighborhood similarity for video annotation. IEEE Transactions on Multimedia 11, 3 (2009), 465--476. Google ScholarDigital Library
- M. Wang, X. S. Hua, X. Yuan, Y. Song, and L. R. Dai. 2007. Optimizing multi-graph learning: Towards a unified video annotation scheme. In Proceedings of the 15th International Conference on Multimedia. ACM, 862--871. Google ScholarDigital Library
- S. Wasserman and K. Faust. 1994. Social Network Analysis: Methods and Applications. Cambridge University Press.Google Scholar
- D. J. Watts. 2002. A simple model of global cascades on random networks. In Proceedings of the National Academy of Sciences. 5766--5771.Google ScholarCross Ref
- K. Q. Weinberger and L. K. Saul. 2009. Distance metric learning for large margin nearest neighbor classification. Journal of Machine Learning Research 10 (2009), 207--244. Google ScholarDigital Library
- J. Weston, C. Leslie, E. Ie, D. Zhou, and A. Elisseeff. 2005. Semi-supervised protein classification using cluster kernels. Bioinformatics 21, 15 (2005), 3241--3247. Google ScholarDigital Library
- J. Woo, J. Son, and H. Chen. 2011. An SIR model for violent topic diffusion in social media. In Proceedings of the 2011 IEEE International Conference on Intelligence and Security Informatics (ISI’11). 15--19.Google Scholar
- B. Wu, E. Zhong, H. Hu, A. Horner, and Q. Yang. 2013. SMART: Semi-supervised music emotion recognition with social tagging. In Proceedings of 2013 SIAM International Conference on Data Mining (SDM’13). ACM.Google Scholar
- J. Xiao, J. Wang, P. Tan, and L. Quan. 2007. Joint affinity propagation for multiple view segmentation. In IEEE 11th International Conference on Computer Vision. IEEE, 1--7.Google Scholar
- E. P. Xing, A. Y. Ng, M. I. Jordan, and S. Russell. 2002. Distance metric learning, with application to clustering with side-information. In Advances in Neural Information Processing Systems 15, Vol. 15. 505--512.Google Scholar
- B. Xu and L. Liu. 2010. Information diffusion through online social networks. In Proceedings of the IEEE International Conference on Emergency Management and Management Sciences. 53--56.Google Scholar
- O. Yagan, D. Qian, J. Zhang, and D. Cochran. 2012. Information diffusion in overlaying social-physical networks. In CISS. 1--6.Google Scholar
- R. Yan, L. Yang, and A. Hauptmann. 2003. Automatically labeling video data using multi-class active learning. In Proceedings of the 9th IEEE International Conference on Computer Vision. IEEE, 516--523. Google ScholarDigital Library
- R. Yan, J. Zhang, L. Yang, and A. Hauptmann. 2006. A discriminative learning framework with pairwise constraints for video object classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 4 (2006), 578--593. Google ScholarDigital Library
- L. Yang. 2006. Distance Metric Learning: A Comprehensive Survey. Retrieved from http://www.cs.cmu.edu/liuy/frame_survey_v2.pdf.Google Scholar
- L. Yang, D. Ji, G. Zhou, Y. Nie, and G. Xiao. 2006. Document re-ranking using cluster validation and label propagation. In Proceedings of the 15th ACM International Conference on Information and Knowledge Management (CIKM’06). ACM, 690--697. Google ScholarDigital Library
- Y. H. Yang, D. Bogdanov, P. Herrera, and M. Sordo. 2012. Music retagging using label propagation and robust principal component analysis. In Proceedings of the 21st International Conference Companion on World Wide Web (WWW’12 Companion). ACM, 869--876. Google ScholarDigital Library
- Z. Yong, L. Weishi, Z. Yang, Z. Gang, Q. Dongxiang, Z. Qi, H. Ying, W. Haifeng, H. Xiaobo, and H. Jiaming. 2013. Brain MRI segmentation with label propagation. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) 2, 5 (2013), 158--163.Google Scholar
- J. Yu, X. Jin, J. Han, and J. Luo. 2011. Collection-based sparse label propagation and its application on social group suggestion from photos. ACM Transaction Intelligent Systems Technology 2, 2, Article 12 (2011), 21 pages. Google ScholarDigital Library
- T. Zhang, C. Xu, G. Zhu, S. Liu, and H. Lu. 2012. A generic framework for video annotation via semi-supervised learning. IEEE Transactions on Multimedia 14, 4 (2012), 1206--1219. Google ScholarDigital Library
- D. Zhou, O. Bousquet, T. N. Lal, J. Weston, and B. Schölkopf. 2004. Learning with local and global consistency. In Advances in Neural Information Processing Systems 16. MIT Press, 321--328.Google Scholar
- D. Zhou and C. J. C. Burges. 2007. Spectral clustering and transductive learning with multiple views. In Proceedings of the 24th International Conference on Machine Learning (ICML’07). ACM, 1159--1166. Google ScholarDigital Library
- D. Zhou, J. Huang, and B. Schölkopf. 2005. Learning from labeled and unlabeled data on a directed graph. In Proceedings of the 22nd International Conference on Machine Learning (ICML’05). ACM, 1036--1043. Google ScholarDigital Library
- D. Zhou, J. Huang, and B. Schölkopf. 2007. Learning with hypergraphs: Clustering, classification, and embedding. Advances in Neural Information Processing Systems 19 (2007), 1601.Google ScholarDigital Library
- D. Zhou and B. Schölkopf. 2004. Learning from labeled and unlabeled data using random walks. In Proceedings of the 26th DAGM Symposium on Pattern Recognition. Springer, 237--244.Google Scholar
- D. Zhou, S. Zhu, K. Yu, X. Song, B. L. Tseng, H. Zha, and C. L. Giles. 2008. Learning multiple graphs for document recommendations. In Proceedings of the 17th International Conference on World Wide Web. ACM, 141--150. Google ScholarDigital Library
- G. D. Zhou and F. Kong. 2009. Global learning of noun phrase anaphoricity in coreference resolution via label propagation. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 (EMNLP’09). 978--986. Google ScholarDigital Library
- X. Zhu. 2008. Semi-Supervised Learning Literature Survey. Technical Report, University of Wisconsin - Madison.Google Scholar
- X. Zhu and Z. Ghahramani. 2002. Learning from Labeled and Unlabeled Data with Label Propagation. Technical Report. School of CS, CMU.Google Scholar
- X. Zhu, Z. Ghahramani, and J. Lafferty. 2003. Semi-supervised learning using gaussian fields and harmonic functions. In ICML. 912--919.Google Scholar
- X. Zhu, J. Kandola, Z. Ghahramani, and J. Lafferty. 2005. Nonparametric transforms of graph kernels for semi-supervised learning. In Advances in Neural Information Processing Systems, Vol. 17. MIT Press, 1641--1648.Google Scholar
- X. Zhu, J. Lafferty, and Z. Ghahramani. 2003. Semi-Supervised Learning: From Gaussian Fields to Gaussian Processes. Technical Report. School of CS, CMU.Google Scholar
- O. Zoidi, N. Nikolaidis, and I. Pitas. 2013. Exploiting clustering and stereo information in label propagation of facial images. In IEEE Symposium Series on Computational Intelligence.Google Scholar
- O. Zoidi, A. Tefas, N. Nikolaidis, and I. Pitas. 2014. Person identity label propagation in stereo videos. IEEE Transactions on Multimedia 16, 5 (2014), 1358--1368.Google ScholarCross Ref
Index Terms
- Graph-Based Label Propagation in Digital Media: A Review
Recommendations
Semi-supervised partial label learning algorithm via reliable label propagation
AbstractPartial label learning (PLL) is a weakly supervised learning method that is able to predict one label as the correct answer from a given candidate label set. In PLL, when all possible candidate labels are as signed to real-world training examples, ...
Transductive Multilabel Learning via Label Set Propagation
The problem of multilabel classification has attracted great interest in the last decade, where each instance can be assigned with a set of multiple class labels simultaneously. It has a wide variety of real-world applications, e.g., automatic image ...
Graph-Based Active Learning Based on Label Propagation
MDAI '08 Sabadell: Proceedings of the 5th International Conference on Modeling Decisions for Artificial IntelligenceBy only selecting the most informative instances for labeling, active learning could reduce the labeling cost when labeled instances are hard to obtain. Facing the same situation, semi-supervised learning utilize unlabeled instances to strengthen ...
Comments