Structured max-margin learning for inter-related classifier training and multilabel image annotation

IEEE Transactions on Image Processing

Volumn 20, Issue 3, 2011, Pages 837-854

  Fan, Jianping ^a   Shen, Yi ^a   Yang, Chunlei ^a   Zhou, Ning ^a  

^a University of North Carolina at Charlotte   (United States)

Author keywords

Inter concept visual similarity; intra concept visual diversity; parallel computing; structured max margin learning; visual concept network

Indexed keywords

INTRA-CONCEPT VISUAL DIVERSITY; PARALLEL COMPUTING; STRUCTURED MAX-MARGIN LEARNING; VISUAL CONCEPT NETWORK; VISUAL SIMILARITY;

CLASSIFIERS; CLUSTERING ALGORITHMS; IMAGE ANALYSIS; PARALLEL ARCHITECTURES;

LEARNING ALGORITHMS;

EID: 79951846242     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2010.2073476     Document Type: Article

References (56)

1. A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, "Content-based image retrieval at the end of the early years," IEEE Trans. PatternAnal. Mach. Intell., vol. 22, no. 12, pp. 1349-1380, Dec. 2000. (Pubitemid 32088465)
2. A. Torralba, R. Fergus, and W. T. Freeman, "80 million tiny images: A large dataset for non-parametric object and scene recognition," IEEE Trans. PatternAnal. Mach. Intell., vol. 30, no. 11, pp. 1958-1970, Nov. 2008.
3. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, andL. Fei-Fei, "ImageNet: A large-scale hierarchical image database," in Proc. IEEE CVPR, 2009, pp. 248-255.
4. M. Naphade, J. R. Smith, J. Tesic, S.-F. Chang, W. Hsu, L. Kennedy, A. Hauptmann, and J. Curtis, "Large-scale concept ontology for multimedia," IEEE Multimedia, 2006.
5. J. Fan, D. Keim, Y. Gao, H. Luo, and Z. Li, "JustClick: Personalized image recommendation via exploratory search from large-scale Flickr images," IEEE Trans. CircuitsSyst. Video Technol., vol. 19, no. 2, pp. 273-288, Feb. 2009.
6. L. Wu, X.-S. Hua, N. Yu, W.-Y. Ma, and S. Li, "Flickr distance," ACM Multimedia, pp. 31-40, 2008.
7. B. C. Russell, A. Torralba, K. P. Murphy, and W. T. Freeman, "LabelMe: A database and web-based tool for image annotation," Int. J. Comput. Vis., vol. 77, no. 1, pp. 157-173, 2008.
8. L. Fei-Fei, R. Fergus, and P. Perona [Online]. Available: http://www. vision.caltech.edu
9. MSRC [Online]. Available: http://research.microsoft.com/
10. D. Lowe, "Distinctive image features from scale invariant keypoints," Int. J. Comput. Vis., vol. 60, pp. 91-110, 2004.
11. P. Quelhas, F. Monay, J.-M. Odobez, D. Gatica-Perez, T. Tuytelaars, and L. J. Van Gool, "Modeling scenes with local descriptors and latent aspects," in Proc. IEEE ICCV, 2005, pp. 883-890. (Pubitemid 44055049)
12. K. Barnard and D. Forsyth, "Learning the semantics of words and pictures," in Proc. IEEE ICCV, 2001, pp. 408-415. (Pubitemid 32795085)
13. N. Vasconcelos, "Image indexing with mixture hierarchies," in Proc. IEEE CVPR, 2001, vol. 1, pp. I-3-I-10.
14. J. Li and J. Z. Wang, "Automatic linguistic indexing of pictures by a statistical modeling approach," IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 9, pp. 1075-1088, Sep. 2003.
15. L. Fei-Fei and P. Perona, "A Bayesian hierarchical model for learning natural scene categories," in Proc. IEEE CVPR, 2005, pp. 524-531.
16. E. Sudderth, A. Torralba, W. Freeman, and A. Willsky, "Learning hierarchical models of scenes, objects, and parts," in Proc. IEEE ICCV, 2005, vol. 2, pp. 1331-1338. (Pubitemid 44042334)
17. J. Luo, A. E. Savakis, and A. Singhal, "A Bayesian network-based framework for semantic image understanding," Pattern Recogn., vol. 38, no. 6, pp. 919-934, 2005. (Pubitemid 40308639)
18. J. Lafferty, A. McCallum, and F. Pereira, "Conditional random field: Probabilistic models for segmenting and labeling sequence data," in Proc. ICML, 2001, pp. 282-289.
19. B. Taskar, C. Guestrin, and D. Koller, "Max-margin Markov networks," in Proc. NIPS, 2003.
20. T. Joachims, T. Finley, and C. Yu, "Cutting-plane training of structural SVMs,"Mach. Learning, vol. 77, no. 1, pp. 27-59, 2009.
21. M. Blaschko and C. Lampert, "Learning to localize objects with structured output regression," in Proc. ECCV, 2008, vol. 5302, LNCS, pp. 2-15.
22. A. Rabinovich, A. Vedaldi, C. Galleguillos, E. Wiewiora, and S. Belongie, "Objects in context," in Proc. IEEE ICCV, 2007, pp. 1-8.
23. J. Shotton, J. Winn, C. Rother, and A. Criminisi, "TexonBoost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context," Int. J. Comput. Vis., pp. 2-23, 2008.
24. J. Tang, X. Hua, M. Wang, Z. Gu, G. Qi, and X. Wu, "Correlative linear neighborhood propagation for video annotation," IEEE Trans. Syst., Man, Cybern. B, vol. 39, no. 2, pp. 409-416, Feb. 2009.
25. S. Kumar and M. Hebert, "Discriminative random fields," Int. J. Comput. Vis., vol. 68, no. 2, pp. 179-201, 2006. (Pubitemid 43724245)
26. W. Jiang, S.-F. Chang, and A. Loui, "Context-based concept fusion with boosted conditional random fields," in Proc. IEEEICASSP, 2007, pp. I-949-I-952.
27. G.-J. Qi, X.-S. Hua, Y. Rui, J. Tang, T. Mei, andH.-J. Zhang, "Correlative multi-label video annotation," ACM Multimedia, pp. 17-26, 2007.
28. A. Torralba, K. P. Murphy, and W. T. Freeman, "Sharing features: Efficient boosting procedures for multi-class object detection," in Proc. IEEE CVPR, 2004, pp. 762-769.
29. J. Fan, Y. Gao, H. Luo, and R. Jain, "Mining multilevel image semantics via hierarchical classification," IEEE Trans. Multimedia, vol. 10, no. 2, pp. 167-187, Feb. 2008.
30. J. Fan, Y. Gao, and H. Luo, "Integrating concept ontology and multitask learning to achieve more effective classifier training for multi-level image annotation," IEEE Trans. Image Process., vol. 17, no. 3, pp. 407-426, Mar. 2008. (Pubitemid 351395085)
31. T. Evgeniou, C. A. Micchelli, and M. Pontil, "Learning multiple tasks with kernel methods," J. Mach. Learning Res., vol. 6, pp. 615-637, 2005.
32. V. Vapnik, The Nature of Statistical Learning Theory. Berlin, Germany: Springer-Verlag, 1995.
33. J. Li, N. Allinson, D. Tao, and X. Li, "Multi-training support vector machine for image retrieval," IEEE Trans. Image Process., vol. 15, no. 11, pp. 3597-3601, 2006. (Pubitemid 44615153)
34. S. Hoi, M. Lyu, and E. Y. Chang, "Learningtheunifiedkernel machines for classification," in Proc. ACMSIGKDD, 2006, pp. 187-196.
35. D. Heckerman, "Bayesian networks for data mining," Data Mining and KnowledgeDiscovery, vol. 1, no. 1, pp. 79-119, 1997. (Pubitemid 127721237)
36. M.I.Jordan,Ed., Learning in Graphical Models. Cambridge, MA: MIT, 1998.
37. S. Sonnenburg, G. Ratsch, C. Schafer, and B. Scholkopf, "Large scale multiple kernel learning," J. Mach. Learning Res., vol. 7, pp. 1531-1565, 2006. (Pubitemid 44373694)
38. G. Smits and E. Jordan, "Improved SVM regression using mixtures of kernels," in Proc. IEEE IJCNN, 2002, pp. 2785-2790. (Pubitemid 34648073)
39. H. W. Kuhn, "The Hungarian method for the assignment problem," Naval Res. Logistics Quarterly, vol. 2, pp. 83-97, 1955.
40. Y. Gao, J. Peng, H. Luo, D. Keim, and J. Fan, "An interactive approach for filtering out junk images from keyword-based Google search results," IEEE Trans. Circuits Syst. Video Technol., vol. 19, no. 12, pp. 1851-1865, Dec. 2009.
41. R. Fergus, P. Perona, and A. Zisserman, "A visual category filter for Google Images," in Proc. ECCV, 2004, pp. 242-256.
42. D. Cai, X. He, Z. Li, W.-Y. Ma, and J.-R. Wen, "Hierarchical clustering of WWW image search results using visual, textual, and link information," ACM Multimedia, pp. 952-959, 2004. (Pubitemid 40211897)
43. J. Shi and J. Malik, "Normalized cuts and image segmentation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 8, pp. 888-905, Aug. 2000.
44. C. Ding, X. He, H. Zha, M. Gu, and H. Simon, "A min-max cut algorithm for graph partitioning and data clustering," in Proc. ICDM, 2001, pp. 107-114.
45. Kumar, A. C. Berg, P. Belhumeur, and S. Nayar, "Attribute and simile classifiers for face verification," in Proc. ICCV, 2009, pp. 365-372.
46. A. Farhadi, I. Endres, D. Hoiem, and D. Forsyth, "Describing objects by their attributes," in Proc. IEEE CVPR, 2009, pp. 1778-1785.
47. C. H. Lampert, H. Nickisch, and S. Harmeling, "Learning to detect unseen object classes by between-class attribute transfer," in Proc. IEEE CVPR, 2009, pp. 951-958.
48. C. Fellbaum, WordNet: An Electronic Lexical Database. Boston, MA: MIT, 1998.
49. Y. Chen, J. Bi, and J. Z. Wang, "MILES: Multiple instance learning via embedded instance selection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 12, pp. 1931-1947, Dec. 2006. (Pubitemid 46405705)
50. Z. Zha, X.-S. Hua, T. Mei, J. Wang, G.-J. Qi, and Z. Wang, "Joint multi-label multi-instance learning for image classification," in Proc. IEEE CVPR, 2008, pp. 1-8.
51. Z.-H. Zhu and M.-L. Zhang, "Multi-instance multi-label learning with application to scene classification," in Proc. NIPS, 2006, pp. 1609-1616.
52. Y. Deng and B. S. Manjunath, "Color image segmentation," in Proc. IEEE CVPR, 1999, pp. 446-451.
53. B. Russell, A. Efros, J. Sivic, W. Freeman, and A. Zisserman, "Using multiple segmentations to discover objects and their extent in image collections," in Proc. IEEE CVPR, 2006, pp. 1605-1614.
54. J. Yang, Y. Liu, E. X. Ping, and A. G. Hauptmann, "Harmonium models for semantic video representation and classification," in Proc. SIAM Conf. Data Mining, 2007.
55. M.-Y. Chen and A. G. Hauptmann, "Discriminative fields for modeling semantic concepts in video," in Proc. RIAO Large-Scale Semantic Access to Content, May 30-Jun. 1 2007.
56. J. Fan, Y. Shen, N. Zhou, and Y. Gao, "Harvesting large-scale weaklytagged image databases from the Web," in Proc. IEEE CVPR, 2010, pp. 802-809.