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  • A comparative study of different texture features for document image retrieval

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    Alaei192493.pdf (959.9Kb)
    Author(s)
    Alaei, Fahimeh
    Alaei, Alireza
    Pal, Umapada
    Blumenstein, Michael
    Griffith University Author(s)
    Alaei, Fahimeh
    Year published
    2019
    Metadata
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    Abstract
    Due to the rapid increase of different digitised documents, there has been significant attention dedicated to document image retrieval over the past two decades. Finding discriminative and effective features is a fundamental task for providing a fast and more accurate retrieval system. Texture features are generally fast to compute and are suitable for large volume data. Thus, in this study, the effectiveness of texture features widely used in the literature of content-based image retrieval is investigated on document images. Twenty-six different texture feature extraction methods from four main categories of texture features, ...
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    Due to the rapid increase of different digitised documents, there has been significant attention dedicated to document image retrieval over the past two decades. Finding discriminative and effective features is a fundamental task for providing a fast and more accurate retrieval system. Texture features are generally fast to compute and are suitable for large volume data. Thus, in this study, the effectiveness of texture features widely used in the literature of content-based image retrieval is investigated on document images. Twenty-six different texture feature extraction methods from four main categories of texture features, statistical, transform, model, and structural-based approaches, are considered in this research work to compare their performance on the problem of document image retrieval. Three document image datasets, MTDB, ITESOFT, and CLEF_IP with various content and page layouts are used to evaluate the twenty-six texture-based features on document image retrieval systems. The retrieval results are computed in terms of precision, recall and F-score, and a comparative analysis of the results is also provided. Feature dimensions and time complexity of the texture-based feature methods are further compared. Finally, some conclusions are drawn and suggestions are made about future research directions.
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    Journal Title
    Expert Systems with Applications
    Volume
    121
    DOI
    https://doi.org/10.1016/j.eswa.2018.12.007
    Copyright Statement
    © 2019 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence, which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Subject
    Mathematical Sciences
    Information and Computing Sciences
    Engineering
    Science & Technology
    Technology
    Computer Science, Artificial Intelligence
    Engineering, Electrical & Electronic
    Operations Research & Management Science
    Publication URI
    http://hdl.handle.net/10072/386761
    Collection
    • Journal articles

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