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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
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Index Terms

Computer Science
Information Sciences

Keywords

Multimodal Fusion Clustering Arm Algorithm.