Hierarchical mutual nearest neighbour image segmentation

S. M. Abdullah, Peter Tischer, Sudanthi Wijewickrema, Andrew Paplinski

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    7 Citations (Scopus)


    This paper presents a hierarchical image segmentation algorithm based on the principle of mutual nearest neighbours. Image segmentation remains a great challenge in the computer vision community. To solve this problem, various algorithms have been proposed in the literature. However, most of these algorithms depend heavily on thresholds or parameter settings. Furthermore, the majority of them do not recognise the hierarchical nature of the problem. In particular, there might not be a single best segmentation for an image as the level of detail that should be present in a segmentation will depend on the purpose for which that segmentation will be used. Many algorithms might provide good results for a specific application, or in detecting a certain level of detail in an image, but may fail when they are applied to different types of images at a different level of detail. The method proposed in this paper generates a hierarchy of segmentations that retain different levels of detail. Thus, depending on the application, the segmentation that provides the required level of detail can be selected. Utilisation of only one meta-parameter in the process makes it applicable to any dataset as is. It can also be easily generalised to segment different types of images including 3D and multispectral images. Evaluation on the Berkeley BSD500 dataset and comparison with existing hierarchical segmentation algorithms provide superior results.

    Original languageEnglish
    Title of host publicationInternational Conference on Digital Image Computing: Techniques and Applications (DICTA 2016)
    Subtitle of host publicationGold Coast, Australia, 30 November - 02 December 2016
    EditorsAlan Wee-Chung Liew, Brian Lovell, Clinton Fookes, Jun Zhou, Yongsheng Gao, Michael Blumenstein, Zhiyong Wang
    Place of PublicationPiscataway, NJ
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Number of pages8
    ISBN (Electronic)9781509028955
    ISBN (Print)9781509028962
    Publication statusPublished - 22 Dec 2016
    EventDigital Image Computing Techniques and Applications 2016 - Mantra on View Hotel, Gold Coast, Australia
    Duration: 30 Nov 20162 Dec 2016
    Conference number: 18th
    https://ieeexplore.ieee.org/xpl/conhome/7794373/proceeding (Proceedings)


    ConferenceDigital Image Computing Techniques and Applications 2016
    Abbreviated titleDICTA 2016
    CityGold Coast
    OtherThe International Conference on Digital Image Computing: Techniques and Applications (DICTA) is the main Australian Conference on computer vision, image processing, pattern recognition, and related areas. DICTA was established in 1991 as the premier conference of the Australian Pattern Recognition Society (APRS).
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