• Login
    About WHOAS
    View Item 
    •   WHOAS Home
    • Woods Hole Oceanographic Institution
    • Applied Ocean Physics and Engineering (AOP&E)
    • View Item
    •   WHOAS Home
    • Woods Hole Oceanographic Institution
    • Applied Ocean Physics and Engineering (AOP&E)
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of WHOASCommunities & CollectionsBy Issue DateAuthorsTitlesKeywordsThis CollectionBy Issue DateAuthorsTitlesKeywords

    My Account

    LoginRegister

    Statistics

    View Usage Statistics

    Dominant run-length method for image classification

    Thumbnail
    View/Open
    WHOI-97-07.pdf (1.746Mb)
    Date
    1997-06
    Author
    Tang, Xiaoou  Concept link
    Metadata
    Show full item record
    Citable URI
    https://hdl.handle.net/1912/382
    DOI
    10.1575/1912/382
    Keyword
     Textue image classification; Run length; Karunen Loeve Transform 
    Abstract
    In this paper, we develop a new run-length texture feature extraction algorithm that significantly improves image classification accuracy over traditional techniques. By directly using part or all of the run-length matrix as a feature vector, much of the texture information is preserved. This approach is made possible by the introduction of a new multi-level dominant eigenvector estimation algorithm. It reduces the computational complexity of the Karhunen-Loeve Transform by several orders of magnitude. Combined with the Bhattacharya distance measure, they form an efficient feature selection algorithm. The advantage of this approach is demonstrated experimentally by the classification of two independent texture data sets. Perfect classification is achieved on the first data set of eight Brodatz textures. The 97% classification accuracy on the second data set of sixteen Vistex images further confirms the effectiveness of the algorithm. Based on the observation that most texture information is contained in the first few columns of the run-length matrix, especially in the first column, we develop a new fast, parallel run-length matrix computation scheme. Comparisons with the co-occurrence and wavelet methods demonstrate that the run-length matrices contain great discriminatory information and that a method of extracting such information is of paramount importance to successful classification.
    Collections
    • Applied Ocean Physics and Engineering (AOP&E)
    • WHOI Technical Reports
    Suggested Citation
    Tang, X. (1997). Dominant run-length method for image classification. Woods Hole Oceanographic Institution. https://doi.org/10.1575/1912/382
     

    Related items

    Showing items related by title, author, creator and subject.

    • Thumbnail

      Our safari in migrating from Universal Decimal Classification (UDC) to Library of Congress Classification (LC) 

      Kilemba, Lucas M. (IAMSLIC, 2012)
    • Thumbnail

      Body length and postoral arm length of Dendraster excentricus larvae measured under three treatments during larval food limitation experiments 

      Pernet, Bruno (Biological and Chemical Oceanography Data Management Office (BCO-DMO). Contact: bco-dmo-data@whoi.edu, 2020-07-27)
      Body length and postoral arm length of Dendraster excentricus larvae measured under three treatments during larval food limitation experiments. For a complete list of measurements, refer to the full dataset description ...
    • Thumbnail

      Spectral feature classification of oceanographic processes using an autonomous underwater vehicle 

      Zhang, Yanwu (Massachusetts Institute of Technology and Woods Hole Oceanographic Institution, 2000-06)
      The thesis develops and demonstrates methods of classifying ocean processes using an underwater moving platform such as an Autonomous Underwater Vehicle (AUV). The "mingled spectrum principle" is established which concisely ...
    All Items in WHOAS are protected by original copyright, with all rights reserved, unless otherwise indicated. WHOAS also supports the use of the Creative Commons licenses for original content.
    A service of the MBLWHOI Library | About WHOAS
    Contact Us | Send Feedback | Privacy Policy
    Core Trust Logo