Image retrieval on the Honeycomb Image Browser

William Plant*, Gerald Schaefer

*Corresponding author for this work

    Research output: Chapter in Book/Published conference outputConference publication

    Abstract

    Efficient and effective approaches of dealing with the vast amount of visual information available nowadays are highly sought after. This is particularly the case for image collections, both personal and commercial. Due to the magnitude of these ever expanding image repositories, annotation of all images images is infeasible, and search in such an image collection therefore becomes inherently difficult. Although content-based image retrieval techniques have shown much potential, such approaches also suffer from various problems making it difficult to adopt them in practice. In this paper, we follow a different approach, namely that of browsing image databases for image retrieval. In our Honeycomb Image Browser, large image databases are visualised on a hexagonal lattice with image thumbnails occupying hexagons. Arranged in a space filling manner, visually similar images are located close together enabling large image datasets to be navigated in a hierarchical manner. Various browsing tools are incorporated to allow for interactive exploration of the database. Experimental results confirm that our approach affords efficient image retrieval.

    Original languageEnglish
    Title of host publication2010 IEEE International Conference on Image Processing, ICIP 2010, proceedings
    PublisherIEEE
    Pages3161-3164
    Number of pages4
    ISBN (Print)978-1-4244-7994-8
    DOIs
    Publication statusPublished - 2010
    Event17th IEEE International Conference on Image Processing - Hong Kong, China
    Duration: 26 Sept 201029 Sept 2010

    Publication series

    NameIEEE Conference Publications
    PublisherIEEE
    ISSN (Print)1522-4880

    Conference

    Conference17th IEEE International Conference on Image Processing
    Abbreviated titleICIP 2010
    Country/TerritoryChina
    CityHong Kong
    Period26/09/1029/09/10

    Keywords

    • content-based image retrieval
    • image database browsing
    • image databases

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