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    Bag-of-features based medical image retrieval via multiple assignment and visual words weighting

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    Type
    Article
    Authors
    Wang, Jingyan
    Li, Yongping
    Zhang, Ying
    Wang, Chao
    Xie, Honglan
    Chen, Guoling
    Gao, Xin cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Computational Bioscience Research Center (CBRC)
    Structural and Functional Bioinformatics Group
    Date
    2011-11
    Permanent link to this record
    http://hdl.handle.net/10754/561909
    
    Metadata
    Show full item record
    Abstract
    Bag-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.
    Citation
    Wang, J., Li, Y., Zhang, Y., Wang, C., Xie, H., Chen, G., & Gao, X. (2011). Notice of Violation of IEEE Publication Principles: Bag-of-Features Based Medical Image Retrieval via Multiple Assignment and Visual Words Weighting. IEEE Transactions on Medical Imaging, 30(11), 1996–2011. doi:10.1109/tmi.2011.2161673
    Sponsors
    The 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.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Transactions on Medical Imaging
    DOI
    10.1109/TMI.2011.2161673
    PubMed ID
    21859616
    ae974a485f413a2113503eed53cd6c53
    10.1109/TMI.2011.2161673
    Scopus Count
    Collections
    Articles; Structural and Functional Bioinformatics Group; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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