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dc.contributor.authorIZQUIERDO, Een_US
dc.date.accessioned2016-09-20T15:00:52Z
dc.date.available2016-09-01en_US
dc.date.issued2016-10-04en_US
dc.date.submitted2016-09-03T17:49:01.970Z
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/15479
dc.description.abstractAbstract—This paper introduces an effective processing framework nominated ICP (Image Cloud Processing) to powerfully cope with the data explosion in image processing field. While most previous researches focus on optimizing the image processing algorithms to gain higher efficiency, our work dedicates to providing a general framework for those image processing algorithms, which can be implemented in parallel so as to achieve a boost in time efficiency without compromising the results performance along with the increasing image scale. The proposed ICP framework consists of two mechanisms, i.e. SICP (Static ICP) and DICP (Dynamic ICP). Specifically, SICP is aimed at processing the big image data pre-stored in the distributed system, while DICP is proposed for dynamic input. To accomplish SICP, two novel data representations named P-Image and Big-Image are designed to cooperate with MapReduce to achieve more optimized configuration and higher efficiency. DICP is implemented through a parallel processing procedure working with the traditional processing mechanism of the distributed system. Representative results of comprehensive experiments on the challenging ImageNet dataset are selected to validate the capacity of our proposed ICP framework over the traditional state-of-the-art methods, both in time efficiency and quality of results.en_US
dc.relation.ispartofIEEE Transactions on Big Dataen_US
dc.rightsAccepted version http://arxiv.org/abs/1607.00577
dc.titleA Hierarchical Distributed Processing Framework for Big Image Dataen_US
dc.typeArticle
dc.rights.holder© IEEE
dc.identifier.doi10.1109/TBDATA.2016.2613992en_US
pubs.notesNot knownen_US
pubs.publication-statusPublisheden_US
dcterms.dateAccepted2016-09-01en_US
qmul.funderLarge Scale Information Exploitation of Forensic Data (LASIE)::European Commissionen_US


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