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dc.contributor.authorWang, Jingyan
dc.contributor.authorLi, Yongping
dc.contributor.authorZhang, Ying
dc.contributor.authorWang, Chao
dc.contributor.authorXie, Honglan
dc.contributor.authorChen, Guoling
dc.contributor.authorGao, Xin
dc.date.accessioned2015-08-03T09:33:52Z
dc.date.available2015-08-03T09:33:52Z
dc.date.issued2011-11
dc.identifier.issn02780062
dc.identifier.pmid21859616
dc.identifier.doi10.1109/TMI.2011.2161673
dc.identifier.urihttp://hdl.handle.net/10754/561909
dc.description.abstractBag-of-features based approaches have become prominent for image retrieval and image classification tasks in the past decade. Such methods represent an image as a collection of local features, such as image patches and key points with scale invariant feature transform (SIFT) descriptors. To improve the bag-of-features methods, we first model the assignments of local descriptors as contribution functions, and then propose a novel multiple assignment strategy. Assuming the local features can be reconstructed by their neighboring visual words in a vocabulary, reconstruction weights can be solved by quadratic programming. The weights are then used to build contribution functions, resulting in a novel assignment method, called quadratic programming (QP) assignment. We further propose a novel visual word weighting method. The discriminative power of each visual word is analyzed by the sub-similarity function in the bin that corresponds to the visual word. Each sub-similarity function is then treated as a weak classifier. A strong classifier is learned by boosting methods that combine those weak classifiers. The weighting factors of the visual words are learned accordingly. We evaluate the proposed methods on medical image retrieval tasks. The methods are tested on three well-known data sets, i.e., the ImageCLEFmed data set, the 304 CT Set, and the basal-cell carcinoma image set. Experimental results demonstrate that the proposed QP assignment outperforms the traditional nearest neighbor assignment, the multiple assignment, and the soft assignment, whereas the proposed boosting based weighting strategy outperforms the state-of-the-art weighting methods, such as the term frequency weights and the term frequency-inverse document frequency weights. © 2011 IEEE.
dc.description.sponsorshipThe work was supported by the Major State Basic Research Development Program of China (973 Program) under Grant 2010CB834303 and a grant from King Abdullah University of Science and Technology.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectBag-of-features
dc.subjectboosting
dc.subjectmedical image retrieval
dc.subjectmultiple assignment
dc.subjectquadratic programming (QP) problem
dc.subjectvisual words weighting
dc.titleBag-of-features based medical image retrieval via multiple assignment and visual words weighting
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentStructural and Functional Bioinformatics Group
dc.identifier.journalIEEE Transactions on Medical Imaging
dc.contributor.institutionShanghai Institute of Applied Physics, Chinese Academy of Science, Shanghai 201800, China
dc.contributor.institutionOGI School of Science and Engineering, Oregon Health and Science University, Beaverton, OR 97006, United States
dc.contributor.institutionZhongshan Hospital, Fudan University, Shanghai 200032, China
kaust.personGao, Xin


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