Object based image ranking using neural networks

Gour C Karmakar, Syed M Rahman, Laurence S Dooley

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    Abstract

    In this paper an object-based image ranking is performed using both supervised and unsupervised neural networks. The features are extracted based on the moment invariants, the run length, and a composite method. This paper also introduces a likeness parameter, namely a similarity measure using the weights of the neural networks. The experimental results show that the performance of image retrieval depends on the method of feature extraction, types of learning, the values of the parameters of the neural networks, and the databases including query set. The best performance is achieved using supervised neural networks for internal query set.
    Original languageEnglish
    Title of host publicationComputational Science – ICCS 2001
    Subtitle of host publicationInternational Conference San Francisco, CA, USA, May 28-30, 2001 Proceedings, Part II
    EditorsVassil N. Alexandrov, Jack J. Dongarra, Benjoe A. Juliano, Rene S. Renner, C. J. Kenneth Tan
    Place of PublicationBerlin Germany
    PublisherSpringer
    Pages281-290
    Number of pages10
    ISBN (Print)3540422331
    DOIs
    Publication statusPublished - 2001
    EventInternational Conference on Computational Science 2001 - San Francisco, United States of America
    Duration: 27 May 200131 May 2001
    Conference number: 1st
    https://link-springer-com.ezproxy.lib.monash.edu.au/book/10.1007/3-540-45718-6#toc (Proceedings)

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer
    Volume2074
    ISSN (Print)0302-9743

    Conference

    ConferenceInternational Conference on Computational Science 2001
    Abbreviated titleICCS 2001
    CountryUnited States of America
    CitySan Francisco
    Period27/05/0131/05/01
    Internet address

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