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Late Semantic Fusion Approaches for Multimedia Information Retrieval with Automatic Tag Generation
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 130 - Number 5 |
| Year of Publication: 2015 |
| Authors: Pooja Shinde, J.V. Shinde |
10.5120/ijca2015906966
|
Pooja Shinde, J.V. Shinde . Late Semantic Fusion Approaches for Multimedia Information Retrieval with Automatic Tag Generation. International Journal of Computer Applications. 130, 5 ( November 2015), 19-23. DOI=10.5120/ijca2015906966
Abstract
Image retrieval in general and content based image retrieval in particular are well known research fields in information retrieval management. An image contains several types of visual information which are difficult to extract and combine manually by humans. The main goal of this paper is to show multimedia information retrieval task using the combination of textual pre-filtering and image re-ranking. The combination of textual and visual techniques and retrieval processes used to develop the multimedia information retrieval system by which we solves the problem of the semantic gap of the given query. Five late semantic fusion approaches are used for text based and content based image retrieval of any dataset. The logistic regression relevance feedback algorithm is used to determine the similarity between the images from the dataset to the query.
References
- Xaro Benavent, Ana Garcia-Serrano, Ruben Granados, Joan Benavent, and Es- ther de Ves,”Multimedia Information Retrieval Based on Late Semantic Fusion Ap-proaches: Experiments on a Wikipedia Image Collection”, IEEE transactions on multimedia, vol.15.
- S. Santini and R. Jain, “Semantic combination of textual and visual information in multimedia retrieval”,IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 9, pp. 871-883, Sept. 1999.
- J. A. AslamandM. Montague “Models for metasearch ”, Proc. IEEE Intl Conf. Multimedia and Expo, July 2005.
- B.J. Jansen, A. Spink, and T. Saracevic, “Real Life, Real Users, and Real Needs: A Study and Analysis of User Queries on the Web”, Information Processing and Management, vol. 36, no. 2, pp. 207-227, 2000.
- R. Datta, D. Joshi, J. Li, and J.Z. Wang, and E.L. Miller, “Image Retrieval: Ideas, Influences, and Trends of the New Age”ACM Computing Surveys, vol. 40, no. 2, pp. 1-60, 2010.
- D. Joshi, J.Z. Wang, and J. Li ,“Semantic combination of textual and visual information in multimedia retrieval ”, Proc. ACM 14th Ann. Intl Conf. Multimedia, 2006.
- J. Li and J. Wang ,“Real-Time Computerized Annotation of Pictures”, Proc. ACM 14th Ann. Intl Conf. Multimedia, 2006.
- T. Hofmann, “Probabilistic Latent Semantic Indexing”, Proc. 22nd Intl Conf. Research and Development in Information Retrieval (SIGIR 99), 1999.
- T. Hofmann ,‘Unsupervised Learning by Probabilistic Latent Semantic Analysis”, Machine Learning, vol. 42, no. 1/2, pp. 177- 196, 2001.
- D.M. Blei and A.Y. Ng, and M.I. Jordan,“Latent Dirichlet Allocation”,J. Machine Learning Research, vol. 3, pp. 993-1022, 2003.
- A. Popescu, T. Tsikrika, and J. Kludas, “Overview of the wikipedia retrieval task at ImageCLEF 2010”,Proc. Natl Academy of Sciences USA, vol. 101, no. suppl. 1, pp. 5228-5235, 2009.
- S. Clinchant, G. Csurka, and J. Ah-Pine, Semantic combination of textual and visual information in multimedia retrieval, in Proc. 1st ACM Int. Conf. Multimedia Retrieval, New York, NY, USA, 2011.
- P. K. Atrey,M. A. Hossain, A. El Saddik, andM. S. Kankanballi,Multimodal Fusion for Multimedia Analysis: A Survey, in Multimedia Syst., vol. 16, pp. 345 379, 2010.
- R. Granados, J. Benavent, X. Benavent, E. de Ves, and A. GarciaSerrano, Multimodal Information Approaches for the Wikipedia Collection at ImageCLEF 2011, in Proc. CLEF 2011 Labs Workshop, Notebook Papers, Amsterdam, The Netherlands, 2011.
- J. Kludas, E. Bruno, and S. Marchand-Maillet, Information fusion in multimedia information retrieval, in AMR Int. Workshop Retrieval User Semantics, 2007.
- T. Tsikrika, A. Popescu, and J. Kludas, Overview of the Wikipedia image retrieval task at ImageCLEF 2011, in Proc. CLEF 2011 Labs Workshop, Notebook Papers, Amsterdam, The Netherlands, 2011
- CHATZICHRISTOFIS, S. A., BOUTALIS, Y. S., LUX,”An interactive content based image retrieval system. In Second International Workshop on Similarity Search and Applications SISAP 2009”. in Prague (Czech Republic), 2009, p. 151 153.
- M. Grubinger,Analysis and Evaluation of Visual Information Systems Performance, Ph.D. thesis, School Comput. Sci. Math., Faculty Health, Engi., Sci., Victoria Univ., Melbourne, Australia, 2007.
- M. Steyvers, P. Smyth, M. Rosen-Zvi, and T. Griffiths,“Probabilistic Author-Topic Models for Information Discovery”,Proc. 10th ACM SIGKDD Conf. Knowledge Discovery and Data Mining, 2004.
- Z. Guo, S. Zhu, Y. Chi, Z. Zhang, and Y. Gong, “A Latent Topic Model for Linked Documents”, Proc. 32nd Intl ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR), 2009.
- T.-T. Pham, N.E. Maillot, J.-H. Lim, and J.-P. Chevallet,“Latent Semantic Fusion Model for Image Retrieval and Annotation”, Proc. 16th ACM Conf. Information and Knowledge Management (CIKM), 2007.
- J. Fan and Y. Gao, and H. Luo, “Integrating Concept Ontology and Multitask Learning to Achieve More Effective Classier Training for Multilevel Image Annotation”,IEEE Trans. Image Processing, vol. 17, no. 3, pp. 407-426, Mar. 2008.
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