Retrieval and Re-Ranking of Images with Better Optimization Using Hyper Graph
Keywords:
Hyper Graph, VCLTR, Natural Language Processing, Data Mining, Information Retrieval, Discounted Cumulative Gain, Fast Alternating Linearization MethodAbstract
The process of image search includes searching of images based on user given keywords. Most of the image retrieval techniques are based on text based image retrievals. But it has certain problems like images are time duplicated, low precision, and irrelevant. This paper represents the multiple scenario may occur due to sparse and noisy textual query. Because of this user cannot be always sure of perfect images being obtained in available time. To moderate image search user clicks are introduced into the search query so that the relevance between given query and result obtained should be maximized. User clicks are integrated to textual features to make refinement of textual query. The major bottleneck is likely mismatch between the image content and the given text. Image search re-ranking attempts to resolve this bottleneck by relying on both the text information and visual information during the image search process. This paper represents the trends in image search reranking and optimization with hyper graph.
References
- H. Li, "Learning to rank for information retrieval and natural language processing," Synth. Lect. Human Lang. Technol., vol. 4, no. 1, pp. 1– 113, 2011.
- S. Robertson and S. Walker, "Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval," in Proc. Annu. Int. ACM SIGIR Conf. Res. Dev. Inf. Retrieval, 1994, pp. 232–241.
- K. Jarvelin and J. Kekalainen, "Cumulated gain-based evaluation of IR techniques," ACM Trans. Inf. Syst., vol. 20, no. 4, pp. 422–446, 2002.
- D. Cossock and T. Zhang, "Statistical analysis of Bayes optimal subset ranking," IEEE Trans. Inf. Theory, vol. 54, no. 11, pp. 5140–5154, Nov. 2008.
- R. Herbrich, T. Graepel, and K. Obermayer, "Large margin rank boundaries for ordinal regression," in Advances in Large Margin Classifiers. Cambridge, MA, USA: MIT Press, 2000, pp. 115–132.
- J. Ye, J.-H. Chow, J. Chen, and Z. Zheng, "Stochastic gradient boosted distributed decision trees," in Proc. ACM Conf. Inf. Knowl. Manag., Hong Kong, 2009, pp. 2061–2064.
- Z. Cao, T. Qin, T.-Y. Liu, M.-F. Tsai, and H. Li, "Learning to rank: From pairwise approach to listwise approach," in Proc. Int. Conf. Mach. Learn., Corvallis, OR, USA, 2007, pp. 129–136.
- F. Xia, T. Y. Liu, J. Wang, W. Zhang, and H. Li, "Listwise approach to learning to rank: Theory and algorithm," in Proc. Int. Conf. Mach. Learn., 2008, pp. 1192–1199.
- X. Hua and G. Qi, "Online multi-label active annotation: Towards large scale content-based video search," in Proc. ACM Int. Conf. Multimedia, 2008, pp. 141–150.
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