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A scalable approach for content based image retrieval in cloud datacenter

Jianxin Liao (liaojx@bupt.edu.cn), Di Yang (yangdi.bupt@gmail.com), Tonghong Li (tonghong@fi.upm.es), Jingyu Wang (wangjingyu@bupt.edu.cn), Qi Qi (qiqi8266@bupt.edu.cn) and Xiaomin Zhu (zhuxm@bupt.edu.cn)
Additional contact information
Jianxin Liao: Beijing University of Posts and Telecommunications
Di Yang: Beijing University of Posts and Telecommunications
Tonghong Li: Technical University of Madrid
Jingyu Wang: Beijing University of Posts and Telecommunications
Qi Qi: Beijing University of Posts and Telecommunications
Xiaomin Zhu: Beijing University of Posts and Telecommunications

Information Systems Frontiers, 2014, vol. 16, issue 1, No 10, 129-141

Abstract: Abstract The emergence of cloud datacenters enhances the capability of online data storage. Since massive data is stored in datacenters, it is necessary to effectively locate and access interest data in such a distributed system. However, traditional search techniques only allow users to search images over exact-match keywords through a centralized index. These techniques cannot satisfy the requirements of content based image retrieval (CBIR). In this paper, we propose a scalable image retrieval framework which can efficiently support content similarity search and semantic search in the distributed environment. Its key idea is to integrate image feature vectors into distributed hash tables (DHTs) by exploiting the property of locality sensitive hashing (LSH). Thus, images with similar content are most likely gathered into the same node without the knowledge of any global information. For searching semantically close images, the relevance feedback is adopted in our system to overcome the gap between low-level features and high-level features. We show that our approach yields high recall rate with good load balance and only requires a few number of hops.

Keywords: Cloud computing; Content based image retrieval; Peer-to-peer; Locality sensitive hashing; Relevance feedback (search for similar items in EconPapers)
Date: 2014
References: View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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DOI: 10.1007/s10796-013-9467-0

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