Multimedia data are used everywhere from huge digital study to the web, multimedia information is used in the professional or personal exercises. Enhancement of multimedia information retrieval can be used both the textual pre-filtering and image re-ranking. The textual and visual techniques are combined and then processes of retrieval are used to develop the multimedia information retrieval system to solve the problem of the semantic gap in the given query. For text based and content based image retrieval, late semantic fusion approaches can also used. The user can also use relevant items that have been found by the system to improve future searches, which is the basis behind logistic regression relevance feedback algorithm is used.

1. XaroBenavent, Ana Garcia-Serrano, Ruben Granados, Joan Benavent, and Esther de Ves, "Multimedia Information Retrieval Based on Late Semantic Fusion Approaches: Experiments on a Wikipedia Image Collection", IEEE Transactions On Multimedia, Vol. 15, No. 8, 2013.
2. S. Clinchant, G. Csurka, and J. Ah-Pine, "Semantic combination of textual and visual information in multimedia retrieval," Proc. 1st ACM Int. Conf. Multi-media Retrieval, New York, NY, USA, 2011.
3. R. Granados, J. Benavent, X. Benavent, E. de Ves, and A. Garcia-Serrano, "Multimodal Information Approaches for the Wikipedia Collection at ImageCLEF 2011," in Proc. CLEF 2011 Labs Workshop, Notebook Papers, Amsterdam, The Netherlands, 2011.
4. Gabriela Csurka, Julien Ah-Pine, and Stéphane Clinchant, "Unsupervised Visual and Textual Information Fusion in Multimedia Retrieval - A Graph-based Point of View," arXiv:1401. 6891v1 [cs. IR] 27 Jan 2014.
5. P. K. Atrey,M. A. Hossain, A. El Saddik, and M. S. Kankanballi, "Multimodal Fusion for Multimedia Analysis: A Survey," Multimedia Syst. , vol. 16, pp. 345 379, 2010.
6. S. A. Chatzichristo_s, K. Zagoris, Y. S. Boutalis, and N. Papamarkos, "Accurate image retrieval based on compact composite descriptors and relevance feedback in-formation," Int. J. Pattern Recog. Artif. Intell. , vol. 24, no. 2, pp. 207 244, 2010.
7. M. Grubinger, "Analysis and Evaluation of Visual Information Systems Perfor-mance," Ph. D. thesis, School Comput. Sci. Math. , Faculty Health, Engi. , Sci. , Victoria Univ. , Melbourne, Australia, 2007.
8. Nagham Hamid, Abid Yahya, R. Badlishah Ahmad, and Osamah M. Al-Qershi, " A Comparison between Using SIFT and SURF for Characteristic Region Based Image Steganography" IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 3, No 3, May 2012.
9. D. G. Lowe, "Distinctive image features from scale-invariant keypoints," International J. Comput. Vision, vol. 60, no. 2, pp. 91–110, 2004.
10. J. A. AslamandM. Montague, "Models for metasearch," in Proc. 24thAnnu. Int. ACM SIGIR Conf. Res. Develop. Inform. Retrieval, New Orleans, LA, USA, 2001, pp. 276–284.
11. M. Montague and J. A. Aslam, "Condorcet fusion for improved retrieval," in Proc 11th Int. ,Conf. Inf, Knowledge Manage, McLean, VA, USA, 2002, pp. 538–548.
12. Y. Rui, S. Huang, M. Ortega, and S. Mehrotra, "Relevance feedback: A power tool for interactive content-based image retrieval," IEEE Trans Circuits Syst. Video Technol. , vol. 8, no. 5, Sep. 1998.
13. Yogesh C. Pathak, S. A. Chhabria, "Performance Evolution of Eye and Hand Fusion for Diagonal Movement Gesture Recognition," International Journal of Advance Research in Computer Science and Management Studies, Volume 2, Issue 4, April 2014.

Image Retrieval Late Fusion Multimedia Information Retrieval.