A scalable approach for content based image retrieval in cloud datacenter

Liao, Jianxin and Yang, Di and Li, Tonghong and Wang, Jingyu and Qi, Qi and Zhu, Xiaomin (2013). A scalable approach for content based image retrieval in cloud datacenter. "Information Systems Frontiers", v. 6 (n. 1); pp. 129-141. ISSN 1387-3326. https://doi.org/10.1007/s10796-013-9467-0.

Description

Title: A scalable approach for content based image retrieval in cloud datacenter
Author/s:
  • Liao, Jianxin
  • Yang, Di
  • Li, Tonghong
  • Wang, Jingyu
  • Qi, Qi
  • Zhu, Xiaomin
Item Type: Article
Título de Revista/Publicación: Information Systems Frontiers
Date: 2013
ISSN: 1387-3326
Volume: 6
Subjects:
Faculty: Facultad de Informática (UPM)
Department: Lenguajes y Sistemas Informáticos e Ingeniería del Software
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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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.

More information

Item ID: 28920
DC Identifier: http://oa.upm.es/28920/
OAI Identifier: oai:oa.upm.es:28920
DOI: 10.1007/s10796-013-9467-0
Official URL: http://www.springer.com/business+%26+management/business+information+systems/journal/10796
Deposited by: Memoria Investigacion
Deposited on: 20 Jan 2015 11:52
Last Modified: 04 Dec 2017 15:53
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