Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.compeleceng.2016.04.015 | Computers & Electrical Engineering |
Keywords | Field | DocType |
CBIR,Feature Extraction,BOVW,Image indexing,Hadoop,MapReduce,Clustering | Data mining,Bag-of-words model in computer vision,Information retrieval,Computer science,Search engine indexing,Image retrieval,Feature extraction,Exploit,Cluster analysis,Big data,Content-based image retrieval | Journal |
Volume | Issue | ISSN |
54 | C | 0045-7906 |
Citations | PageRank | References |
7 | 0.44 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Noha A. Sakr | 1 | 7 | 0.44 |
| Ali I. Eldesouky | 2 | 36 | 6.97 |
| Hesham Arafat | 3 | 13 | 2.58 |