Techniques for Content-based Image Characterization in Wavelets Domain

  • Georgios Voulgaris

    Student thesis: Doctoral Thesis


    This thesis documents the research which has led to the design of a number of techniques aiming to improve the performance of content-based image retrieval (CBIR) systems in wavelets domain using texture analysis. Attention was drawn on CBIR in transform domain and in particular wavelets because of the excellent characteristics for compression and texture extraction applications and the wide adoption in both the research community and the industry. The issue of performance is addressed in terms of accuracy and speed.

    The rationale for this research builds upon the conclusion that CBIR has not yet reached a good performance balance of accuracy, efficiency and speed for wide adoption in practical applications. The issue of bridging the sensory gap, which is defined as "[the difference] between the object in the real world and the information in a (computational) description derived from a recording of that scene." has yet to be resolved. Furthermore, speed improvement remains an uncharted territory as is feature extraction directly from the bitstream of compressed images.

    To address the above requirements the first part of this work introduces three techniques designed to jointly address the issue of accuracy and processing cost of texture characterization in wavelets domain. The second part introduces a new model for mapping the wavelet coefficients of an orthogonal wavelet transformation to a circular locus. The model is applied in order to design a novel rotation-invariant texture descriptor. All of the aforementioned techniques are also designed to bridge the gap between texture-based image retrieval and image compression by using appropriate compatible design parameters. The final part introduces three techniques for improving the speed of a CBIR query through more efficient calculation of the Li-distance, when it is used as an image similarity metric. The contributions conclude with a novel technique which, in conjunction with a widely adopted wavelet-based compression algorithm, extracts texture information directly from the compressed bit-stream for speed and storage requirements savings. The experimental findings indicate that the proposed techniques form a solid groundwork which can be extended to practical applications
    Date of AwardJun 2008
    Original languageEnglish
    SupervisorPaul Jarvis (Supervisor)


    • Wavelets Domain
    • content-based image retrieval (CBIR) systems
    • performance

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