Modeling multiple visual words assignment for bag-of-features based medical image retrieval

Handle URI:
http://hdl.handle.net/10754/564483
Title:
Modeling multiple visual words assignment for bag-of-features based medical image retrieval
Authors:
Wang, Jim Jing-Yan; Almasri, Islam
Abstract:
In this paper, we investigate the bag-of-features based medical image retrieval methods, which represent an image as a collection of local features, such as image patch and key points with SIFT descriptor. To improve the bag-of-features method, we first model the assignment of local descriptor as contribution functions, and then propose a new multiple assignment strategy. By assuming the local feature can be reconstructed by its neighboring visual words in vocabulary, we solve the reconstruction weights as a QP problem and then use the solved weights as contribution functions, which results in a new assignment method called the QP assignment. We carry our experiments on ImageCLEFmed datasets. Experiments' results show that our proposed method exceeds the performances of traditional solutions and works well for the bag-of-features based medical image retrieval tasks.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program; Computational Bioscience Research Center (CBRC)
Publisher:
ACTA Press
Journal:
Signal Processing, Pattern Recognition and Applications / 779: Computer Graphics and Imaging
Conference/Event name:
IASTED International Conference on Computer Graphics and Imaging, CGIM 2012
Issue Date:
2012
DOI:
10.2316/P.2012.779-015
Type:
Conference Paper
ISBN:
9780889869219
Appears in Collections:
Conference Papers; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorWang, Jim Jing-Yanen
dc.contributor.authorAlmasri, Islamen
dc.date.accessioned2015-08-04T07:02:09Zen
dc.date.available2015-08-04T07:02:09Zen
dc.date.issued2012en
dc.identifier.isbn9780889869219en
dc.identifier.doi10.2316/P.2012.779-015en
dc.identifier.urihttp://hdl.handle.net/10754/564483en
dc.description.abstractIn this paper, we investigate the bag-of-features based medical image retrieval methods, which represent an image as a collection of local features, such as image patch and key points with SIFT descriptor. To improve the bag-of-features method, we first model the assignment of local descriptor as contribution functions, and then propose a new multiple assignment strategy. By assuming the local feature can be reconstructed by its neighboring visual words in vocabulary, we solve the reconstruction weights as a QP problem and then use the solved weights as contribution functions, which results in a new assignment method called the QP assignment. We carry our experiments on ImageCLEFmed datasets. Experiments' results show that our proposed method exceeds the performances of traditional solutions and works well for the bag-of-features based medical image retrieval tasks.en
dc.publisherACTA Pressen
dc.subjectBag-of-featuresen
dc.subjectMedical image retrievalen
dc.subjectMultiple assignmenten
dc.subjectQuadratic programmingen
dc.titleModeling multiple visual words assignment for bag-of-features based medical image retrievalen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.identifier.journalSignal Processing, Pattern Recognition and Applications / 779: Computer Graphics and Imagingen
dc.conference.date18 June 2012 through 20 June 2012en
dc.conference.nameIASTED International Conference on Computer Graphics and Imaging, CGIM 2012en
dc.conference.locationCreteen
kaust.authorWang, Jim Jing-Yanen
kaust.authorAlmasri, Islamen
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