An efficient fast-response content-based image retrieval framework for big data

In this paper, an efficient fast-response content-based image retrieval (CBIR) framework based on Hadoop MapReduce is proposed to operate stably with high performance targeting big data. It provides a novel bag of visual words (BOVW) technique based on a proposed chain-clustering binary search-tree...

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Bibliographic Details
Published in:Computers & electrical engineering 2016-08, Vol.54, p.522-538
Main Authors: Sakr, Noha A., ELdesouky, Ali.I., Arafat, Hesham
Format: Article
Language:English
Subjects:
BOVW
CBIR
Clustering
Feature Extraction
Hadoop
Image indexing
MapReduce
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Summary:In this paper, an efficient fast-response content-based image retrieval (CBIR) framework based on Hadoop MapReduce is proposed to operate stably with high performance targeting big data. It provides a novel bag of visual words (BOVW) technique based on a proposed chain-clustering binary search-tree (CC-BST) algorithm to build the visual statements for representing the image. As well, it introduces a proposed methodology for creating representatives for these visual statements as a solution for big-data' high-dimensionality. Further, those representatives are utilized to provide an indexing scheme for building one large file as an input for Hadoop. Moreover, an efficient-MapReduce technique is presented to exploit the created visual-representatives of the images to retrieve the top-relevant images for the input query. Empirical tests for the proposed techniques outperform the state-of-art compared techniques.
ISSN:0045-7906
1879-0755
DOI:10.1016/j.compeleceng.2016.04.015