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

作者：Sakr Noha A^*; ELdesouky Ali I; Arafat Hesham

来源：Computers & Electrical Engineering, 2016, 54: 522-538.

DOI：10.1016/j.compeleceng.2016.04.015

• CBIR
• Feature Extraction
• BOVW
• Image indexing
• Hadoop
• MapReduce
• Clustering

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.

• 出版日期2016-8