Learning mixtures of submodular functions for image collection summarization

Advances in Neural Information Processing Systems

Volumn 2, Issue January, 2014, Pages 1413-1421

  Tschiatschek, Sebastian ^a   Iyer, Rishabh ^b   Wei, Haochen ^c   Bilmes, Jeff ^a  

^a GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

^b UNIVERSITY OF WASHINGTON   (United States)

^c LINKEDIN CORPORATION   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

INFORMATION SCIENCE;

AMAZON MECHANICAL TURKS; DEEP NEURAL NETWORKS; IMAGE COLLECTIONS; IMAGE SUMMARIZATION; LEARNING APPROACH; STRUCTURED PREDICTION; SUBMODULAR FUNCTIONS; SUBMODULAR OPTIMIZATIONS;

MIXTURES;

EID: 84937855935     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (35)

1. A. Borodin, H. C. Lee, and Y. Ye. Max-sum diversification, monotone submodular functions and dynamic updates. In Proc. of the 31st symposium on Principles of Database Systems, pages 155-166. ACM, 2012.
2. N. Buchbinder, M. Feldman, J. Naor, and R. Schwartz. Submodular maximization with cardinality constraints. In SODA, 2014.
3. J. Carbonell and J. Goldstein. The use of MMR, diversity-based reranking for reordering documents and producing summaries. In Research and Development in Information Retrieval, pages 335-336, 1998.
4. K. Chatfield, V. Lemtexpitsky, A. Vedaldi, and A. Zisserman. The devil is in the details: an evaluation of recent feature encoding methods. In British Machine Vision Conference (BMVC), 2011.
5. T. Denton, M. Demirci, J. Abrahamson, A. Shokoufandeh, and S. Dickinson. Selecting canonical views for view-based 3-d object recognition. In ICPR, Volume 2, pages 273-276, Aug 2004.
6. J. Duchi, E. Hazan, and Y. Singer. Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research (JMLR), 12: 2121-2159, July 2011.
7. R. Fergus, P. Perona, and A. Zisserman. Object class recognition by unsupervised scale-invariant learning. In CVPR, Volume 2, pages 264-271. IEEE, June 2003.
8. Y. Hadi, F. Essannouni, and R. O. H. Thami. Video summarization by k-medoid clustering. In Symposium on Applied Computing (SAC), pages 1400-1401, 2006.
9. T. Hastie, R. Tibshirani, and J. Friedman. The elements of statistical learning, Volume 2. Springer New York, 2nd edition, 2009.
10. R. Iyer and J. Bilmes. Algorithms for approximate minimization of the difference between submodular functions, with applications. In UAI, 2012.
11. R. Iyer, S. Jegelka, and J. Bilmes. Fast semidifferential-based submodular function optimization. In ICML, 2013.
12. A. Jaffe, M. Naaman, T. Tassa, and M. Davis. Generating summaries and visualization for large collections of geo-referenced photographs. In MIR, pages 89-98. ACM, 2006.
13. A. Khosla, R. Hamid, C.-J. Lin, and N. Sundaresan. Large-scale video summarization using web-image priors. In Conference on Computer Vision and Pattern Recognition (CVPR), June 2013.
14. K. Kirchhoff and J. Bilmes. Submodularity for data selection in machine translation. In Empirical Methods in Natural Language Processing (EMNLP), October 2014.
15. A. Kulesza and B. Taskar. k-DPPs: Fixed-size determinantal point processes. In Proceedings of the 28th International Conference on Machine Learning, 2011.
16. X. Li, L. Chen, L. Zhang, F. Lin, and W.-Y. Ma. Image annotation by large-scale content-based image retrieval. In Proc. 14th ann. ACM Int. Conf. on Multimedia, pages 607-610. ACM, 2006.
17. C.-Y. Lin. Rouge: A package for automatic evaluation of summaries. In Text Summarization Branches Out: Proceedings of the ACL-04 Workshop, pages 74-81, July 2004.
18. H. Lin and J. Bilmes. Multi-document summarization via budgeted maximization of submodular functions. In NAACL, 2010.
19. H. Lin and J. Bilmes. A class of submodular functions for document summarization. In ACL/HLT-2011, Portland, OR, June 2011.
20. H. Lin and J. Bilmes. Learning mixtures of submodular shells with application to document summarization. In Conference on Uncertainty in Artificial Intelligence (UAI), pages 479-490, 2012.
21. D. Lowe. Object recognition from local scale-invariant features. In International Conference on Computer Vision (ICCV), Volume 2, pages 1150-1157, 1999.
22. M. Meeker and L. Wu. Internet trends. Technical report, Kleiner Perkins Caufield & Byers, 2013.
23. M. Minoux. Accelerated greedy algorithms for maximizing submodular set functions. Optimization Techniques, pages 234-243, 1978.
24. M. Narasimhan and J. Bilmes. A submodular-supermodular procedure with applications to discriminative structure learning. In Conference on Uncertainty in Artificial Intelligence (UAI), Edinburgh, Scotland, July 2005. Morgan Kaufmann Publishers.
25. G. Nemhauser, L. Wolsey, and M. Fisher. An analysis of approximations for maximizing submodular set functions-i. Mathematical Programming, 14(1): 265-294, 1978.
26. C.-W. Ngo, Y.-F. Ma, and H. Zhang. Automatic video summarization by graph modeling. In International Conference on Computer Vision (ICCV), pages 104-109 Vol.1, Oct 2003.
27. P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun. OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks. ArXiv e-prints, Dec. 2013.
28. I. Simon, N. Snavely, and S. M. Seitz. Scene Summarization for Online Image Collections. ICCV, 2007.
29. P. Sinha and R. Jain. Extractive summarization of personal photos from life events. In International Conference on Multimedia and Expo (ICME), pages 1-6, 2011.
30. P. Sinha, S. Mehrotra, and R. Jain. Summarization of personal photologs using multidimensional content and context. In International Conference on Multimedia Retrieval (ICMR), pages 1-8, 2011.
31. J. Sivic, B. Russell, A. Efros, A. Zisserman, and W. Freeman. Discovering objects and their location in images. In International Conference on Computer Vision (ICCV), Volume 1, pages 370-377, Oct 2005.
32. M. J. Swain and D. H. Ballard. Color indexing. Int. journal of computer vision, 7(1): 11-32, 1991.
33. A. Vedaldi and B. Fulkerson. VLFeat: An open and portable library of computer vision algorithms. http://www.vlfeat.org/, 2008.
34. C. Wengert, M. Douze, and H. Jégou. Bag-of-colors for improved image search. In International Conference on Multimedia (MM), pages 1437-1440. ACM, 2011.
35. J. Yang, K. Yu, Y. Gong, and T. S. Huang. Linear spatial pyramid matching using sparse coding for image classification. In CVPR, pages 1794-1801, 2009.