CFP last date
20 June 2024
Reseach Article

A Review on Association Rules Mining for Image Retrieval using Multimodal Fusion Method

Published on November 2015 by Sonal W.thakare, Sapna R.tayde
National Conference on Recent Trends in Mobile and Cloud Computing
Foundation of Computer Science USA
NCRMC2015 - Number 1
November 2015
Authors: Sonal W.thakare, Sapna R.tayde
ee344a27-2af1-49c6-8f09-66963907ebbb

Sonal W.thakare, Sapna R.tayde . A Review on Association Rules Mining for Image Retrieval using Multimodal Fusion Method. National Conference on Recent Trends in Mobile and Cloud Computing. NCRMC2015, 1 (November 2015), 7-9.

@article{
author = { Sonal W.thakare, Sapna R.tayde },
title = { A Review on Association Rules Mining for Image Retrieval using Multimodal Fusion Method },
journal = { National Conference on Recent Trends in Mobile and Cloud Computing },
issue_date = { November 2015 },
volume = { NCRMC2015 },
number = { 1 },
month = { November },
year = { 2015 },
issn = 0975-8887,
pages = { 7-9 },
numpages = 3,
url = { /proceedings/ncrmc2015/number1/23308-2901/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Recent Trends in Mobile and Cloud Computing
%A Sonal W.thakare
%A Sapna R.tayde
%T A Review on Association Rules Mining for Image Retrieval using Multimodal Fusion Method
%J National Conference on Recent Trends in Mobile and Cloud Computing
%@ 0975-8887
%V NCRMC2015
%N 1
%P 7-9
%D 2015
%I International Journal of Computer Applications
Abstract

The search based on the text query is to be performed firstly in image re-ranking. Then it returned list of images is according to the visual features similarity reordered. The proposed retrieving method in this paper uses the fusion of the images multimodal information i. e. textual and visual which is a current trend in image retrieval researches. For this association rule mining techniques is used. To improve the retrieval accuracy of content-based image retrieval systems, in proposed system has tries to focus on designing sophisticated low-level feature extraction algorithms to narrowing the 'semantic gap' between the visual features and the richness of human semantics. Proposed worked trying to increased the image retrieval performance by fusing i. e. textual and visual features for retrieving and to reduced the semantic gap problem . The obtained results show that the proposed method achieved the best precision score among different query categories. A Multimodal Fusion method based on Association Rules mining (MFAR) is presented in this paper. It is assume as a late fusion and it uses two different data mining techniques for retrieving images: clustering and association rules mining (ARM) algorithm. In this method there are two main phases: offline and online phase. In the offline phase, The Semantic Association Rules is used to identified the relations among the clusters different modalities in the offline phase. At online phase (retrieving phase) uses the generated ARM, to retrieve the related images of search query.

References
  1. Raniah A. Alghamdi, MouniraTaileb, Mohammad Ameen, Faculty of Computing and Information Technology, King Abdulaziz University, Saudi Arabia – Jeddah, ?A New Multimodal Fusion Method Based on Association Rules Mining for Image Retrieval?, 17th IEEE Mediterranean Electrotechnical Conference, Beirut, Lebanon, 13-16 April 2014
  2. Y. Liu, D. Zhanga, G. Lua, and W-Y. Ma , "A survey of content-based image retrieval with high-level semantics", Pattern Recognition, Vol. 40, No. 1. (2007), pp. 262-282.
  3. R. Datta, D. Joshi, J. LI, and J. Z. Wang, "Image retrieval: Ideas,influences, and trends of the new age", ACM Computing Surveys (CSUR), April 2008, 40(2):1-60.
  4. S. Wu and S. McClean, "Performance prediction of data fusion for information retrieval". Information Processing, Management, 2006. 42(4): p. 899-915.
  5. H. Müller, P. Clough, Th. Deselaers, B. Caputo, ImageCLEF(ser. The Springer International Series on Information Retrieval), vol. 32, pp. 95 -114, 2010, Springer-Verlag.
  6. M. Ferecatu and H. Sahbi, "TELECOM ParisTechatImageClefphoto 2008: Bi–modal text and image retrieval with diversity enhancement". In: Working Notes of CLEF 2008, Aarhus, Denmark.
  7. T. Deselaers, T. Weyand, and H. Ney, "Image retrieval and annotation using maximum entropy", in Evaluation of Multilingual and Multi modal Information Retrieval, 2007, pp. 725-734.
  8. T. Gass, T. Weyand, T. Deselaers, and H. Ney. "FIRE in ImageCLEF 2007: Support vector machines and logistic models to fuse image descriptors for photo retrieval". In: CLEF 2007 Proceedings. Lecture Notes in Computer Science (LNCS), 2007,vol 5152. Springer, pp 492–499.
  9. K. Zagoris , A. Arampatzis, S. A. Chatzichristofis,www. MMRetrieval. net: a multimodal search engine", Proceedings of the Third International Conference on Similarity Search and Applications, 2010, Turkey.
  10. J. Ah-Pine, M. Bressan, S. Clinchant, G. Csurka,, Y. Hoppenot,and J. Renders. "Crossing textual and visua content in different application scenarios", Multimedia Tools and Applications, 42(1):31–56, 2009.
  11. R. He, N. Xiong, L. Yang, J. Park, "Using multi-modal semantic association rules to fuse keywords and visual features automatically for web image retrieval". In: International conference on information fusion. 2011.
  12. M. Taileb, S. Lamrous and S. Touati, "Non OverlappingHierarchical Index Struture", International Journal of Computer Science, vol. 3 no. 1, pp. 29-35, 2008.
Index Terms

Computer Science
Information Sciences

Keywords

Multimodal Fusion Clustering Arm Algorithm.