Zhongang Qi
ABSTRACT
In this paper, we study the problem of mining partially annotated images. We first define what the problem of mining partially annotated images is, and argue that in many real-world applications annotated images are typically partially annotated and thus that the problem of mining partially annotated images exists in many situations. We then propose an effective solution to this problem based on a statistical model we have developed called the Semi-Supervised Correspondence Hierarchical Dirichlet Process (SSCHDP). The main idea of this model lies in exploiting the information pertaining to partially annotated images or even unannotated images to achieve semi-supervised learning under the HDP structure. We apply this model to completing the annotations appropriately for partially annotated images in the training data and then to predicting the annotations appropriately and completely for all the unannotated images either in the training data or in any unseen data beyond the training process. Experiments show that SSC-HDP is superior to the peer models from the recent literature when they are applied to solving the problem of mining partially annotated images.
- http://www.fruitfly.org/.Google Scholar
- K. Barnard, P. Duygulu, D. Forsyth, N. de Freitas, D. M. Blei, and M. I. Jordan. Matching words and pictures. Journal of Machine Learning Research, 3:1107--1135, 2003. Google ScholarDigital Library
- D. M. Blei and M. I. Jordan. Modeling annotated data. In Proceedings of the 26th International ACM SIGIR Conference, 2003. Google ScholarDigital Library
- D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 3:993--1022, 2003. Google ScholarDigital Library
- M. R. Boutell, J. Luo, X. Shen, and C. M. Brown. Learning multi-label scene classification. Pattern Recognition, 37(9):1757--1771, 2004.Google ScholarCross Ref
- T. S. Chua, J. Tang, R. Hong, H. Li, Z. Luo, and Y. Zheng. Nus-wide: a real-world web image database from national university of singapore. In Proceedings of the ACM International Conference on Image and Video Retrieval, pages 1--9, 2009. Google ScholarDigital Library
- R. Datta, D. Joshi, J. Li, and J. Z. Wang. Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys, 40:1--60, 2008. Google ScholarDigital Library
- M. Everingham, L. V. Gool, C. Williams, C. K. I., J. Winn, and A. Zisserman. The pascal visual object classes (voc) challenge. International Journal of Computer Vision, 88(2):303--338, 2010. Google ScholarDigital Library
- S. Feng, R. Manmatha, and V. Lavrenko. Multiple bernoulli relevance models for image and video annotation. In Proceedings of International Conference on Computer Vision and Pattern Recognition, pages 1002--1009, 2004. Google ScholarDigital Library
- Z. Guo, Z. Zhang, E. P. Xing, and C. Faloutsos. Enhanced max margin learning on multimodal data mining in a multimedia database. In Proceedings of 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2007. Google ScholarDigital Library
- H. Ishwaran and L. F. James. Gibbs sampling methods for stick-breaking priors. Journal of the American Statistical Association, 96(453):161--173, 2001.Google ScholarCross Ref
- J. Jeon, V. Lavrenko, and R. Manmatha. Automatic image annotation and retrieval using cross-media relevance models. In Proceedings of ACM Special Interest Group on Information Retrieval, pages 119--126, 2003. Google ScholarDigital Library
- V. Lavrenko, R. Manmatha, and J. Jeon. A model for learning the semantics of pictures. In Proceedings of Neural Information Processing Systems, 2003.Google Scholar
- L.-J. Li and L. Fei-Fei. What, where and who? classifying events by scene and object recognition. In Proceedings of International Conference Computer Vision, 2007.Google ScholarCross Ref
- W. Li and M. Sun. Semi-supervised learning for image annotation based on conditional random fields. In Proceedings of ACM International Conference on Image and Video Retrieval, pages 463--472, 2006. Google ScholarDigital Library
- Z. Li, J. Liu, X. Zhu, T. Liu, and H. Lu. Image annotation using multi-correlation probabilistic matrix factorization. In Proceedings of ACM international conference on Multimedia, pages 1187--1190, 2010. Google ScholarDigital Library
- P. Liang, Petrov, M. I. Jordan, and D. Klein. The infinite pcfg using hierarchical dirichlet processes. In Proceedings of Empirical Methods in Natural Language Processing, pages 688--697, 2007.Google Scholar
- B. Liu, W. S. Lee, P. S. Yu, and X. Li. Partially supervised classification of text documents. In Proceedings of the 19th International Conference on Machine Learning, pages 387--394, 2002. Google ScholarDigital Library
- N. Loeff, A. Farhadi, I. Endres, and D. A. Forsyth. Unlabeled data improves word prediction. In Proceedings of International Conference Computer Vision, 2009.Google Scholar
- D. G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91--110, 2004. Google ScholarDigital Library
- D. G. Luenberger and Y. Ye. Linear and Nonlinear Programming. Springer, third edition, 2008.Google ScholarCross Ref
- J. Sethuraman. A constructive definition of dirichlet priors. Statistica Sinica, 4:639--650, 1994.Google Scholar
- A. Sharma, G. Hua, Z. Liu, and Z. Zhang. Meta-tag propagation by co-training an ensemble classifier for improving image search relevance. In Computer Vision and Pattern Recognition Workshop, pages 1--6, 2008.Google ScholarCross Ref
- Y.-Y. Sun, Y. Zhang, and Z.-H. Zhou. Multi-label learning with weak label. In Proceedings of Association for the Advancement of Artificial Intelligence, pages 593--598, 2010.Google Scholar
- Y. W. Teh, M. I. Jordan, M. J. Beal, and D. M. Blei. Hierarchical dirichlet processes. Journal of the American Statistical Association, 101:1566--1581, 2004.Google ScholarCross Ref
- O. Yakhnenko and V. Honavar. Annotating images and image objects using a hierarchical dirichlet process model. In Proceedings of the 9th International Workshop on Multimedia Data Mining, pages 1--7, 2008. Google ScholarDigital Library
- S.-H. Yang, H. Zha, and B.-G. Hu. Dirichlet-bernoulli alignment: A generative model for multi-class multi-label multi-instance corpora. In Proceedings of Neural Information Processing Systems, pages 2143--2150, 2009.Google Scholar
- R. Zhang, Z. Zhang, M. Li, W.-Y. Ma, and H.-J. Zhang. A probabilistic semantic model for image annotation and multi-modal image retrieval. In Proceedings of International Conference Computer Vision, pages 846--851, 2005. Google ScholarDigital Library
- Z.-H. Zhou and M.-L. Zhang. Multi-instance multilabel learning with application to scene classification. In Proccedings of Neural Information Processing Systems, pages 1609--1616, 2007.Google Scholar
- X. Zhu. Semi-supervised learning literature survey. Technical report, Computer Sciences TR 1530, University of Wisconsin-Madison, 2005.Google Scholar
Index Terms
- Mining partially annotated images
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