Call for Paper - March 2023 Edition
IJCA solicits original research papers for the March 2023 Edition. Last date of manuscript submission is February 20, 2023. Read More

Informative Multimedia QA using Web based Approach

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Darshana D. Ambatkar, Vaishali Pujari

Darshana D Ambatkar and Vaishali Pujari. Article: Informative Multimedia QA using Web based Approach. International Journal of Computer Applications 133(13):4-7, January 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Darshana D. Ambatkar and Vaishali Pujari},
	title = {Article: Informative Multimedia QA using Web based Approach},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {133},
	number = {13},
	pages = {4-7},
	month = {January},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


Automated question answering (QA) still faces challenges such as processing and deep understanding of complex questions. In some cases, human intelligence obtains better results than automated approach. The result shows that community question answering (CQA) emerged as an extremely popular alternative to obtain information in which users are able to obtain better answers provided by other participants. But existing CQA forums mostly support textual answers, which are not informative enough for many questions. In this paper we propose a system that enriches textual answers with corresponding media data in Community QA. Our model consists of three components; query analysis for multimedia search, answer medium selection and multimedia data selection and presentation. This system automatically determines which type of media information should be added for textual answer by collecting data from web to enrich the answer. Multimedia QA scheme uses diversification methods to collect the best suitable answers based on questions and make the enriched media data more diverse. It determines the type of medium to be used by adding Nave Bayes Classifier, which helps to generate queries based on existing QA dataset pool and performs multimedia search. This scheme also performs query adaptive re-ranking and redundancy removal to obtain a set of images and videos for presentation accompanying textual answer. It uses Page Ranking algorithm which result shows that it provides more satisfactory and effective results


  1. Nie, Liqiang, et al. "Beyond text QA: Multimedia answer generation by harvesting Web information." Multimedia, IEEE Transactions on 15.2 (2013): 426-441.
  2. Chua, Tat-Seng, et al. "From text question-answering to multimedia QA on web-scale media resources." Proceedings of the First ACM workshop on Large-scale multimedia retrieval and mining. ACM, 2009.
  3. Nie, Liqiang, et al. "Multimedia answering: enriching text QA with media information." Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval. ACM, 2011.
  4. Moschitti, Alessandro, and Silvia Quarteroni. "Linguistic kernels for answer re-ranking in question answering systems." Information Processing & Management 47.6 (2011): 825-842.
  5. Hsu, Chih-Hao, et al. "Using domain ontology to implement a frequently asked questions system." Computer Science and Information Engineering, 2009 WRI World Congress on. Vol. 4. IEEE, 2009.
  6. R. C. Wang, N. Schlaefer, W. W. Cohen, and E. Nyberg, “Automatic set expansion for list question answering,” in Proc. Int. Conf. Empirical Methods in Natural Language Processing, 2008.
  7. E. Parzen and F. Hoti, “On Estimation of a Probability Density Function and Mode,” Annals ofMathematical Statistics, vol. 33, no.3, 1962.
  8. M. Wang and X. S. Hua, “Active learning in multimedia annotation and retrieval: A survey,” ACM Trans. Intell. Syst. Technol.,vol. 2, no. 2, pp. 10–31, 2011
  9. Y. Gao, M. Wang, Z. J. Zha, Q. Tian, Q. Dai, and N. Zhang, “Less is more: Efficient 3d object retrieval with query view selection,”IEEE Trans. Multimedia, vol. 13, no. 5, pp. 1007–1018, 2011.
  10. I. Ahmad and T.-S. Jang, “Old fashion text-based image retrieval using FCA,” in Proc. ICIP, 2003.
  11. D. Liu et al., “Tag Ranking,” Proc. 18th Int‟l Conf.World Wide Web, ACM Press, 2009, pp. 351-360.
  12. X. Tian, L. Yang, J. Wang, Y. Yang, X. Wu, and X.-S. Hua, “Bayesian video search reranking,” in Proc. ACM Int. Conf.Multimedia, 2008.
  13. Liu, Shaowei, et al. "Social visual image ranking for web image search."Advances in Multimedia Modeling. Springer Berlin Heidelberg, 2013. 239-249.
  14. H. Feng, A. Chandrashekhara, and T.-S. Chua, Tamra:” An Automatic Temporal Multiresolution Analysis
  15. Framework for Shot Boundary Detection, Proc. Int‟l Conf. Multimedia Modeling (MMM), ACM Press,2003
  16. .S. Lazebnik, C. Schmid, and J. Ponce, “Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural SceneCategories,” Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition (CVPR), IEEE CS Press, 2006.
  17. D.R. Radev et al., “Evaluating Web-based Question Answering Systems,” Proc. Int‟l Conf. Language Resources and Evaluation,2002


CQA, medium selection, question answering, Reranking