• myGriffith
    • Staff portal
    • Contact Us⌄
      • Future student enquiries 1800 677 728
      • Current student enquiries 1800 154 055
      • International enquiries +61 7 3735 6425
      • General enquiries 07 3735 7111
      • Online enquiries
      • Staff phonebook
    View Item 
    •   Home
    • Griffith Research Online
    • Journal articles
    • View Item
    • Home
    • Griffith Research Online
    • Journal articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

  • All of Griffith Research Online
    • Communities & Collections
    • Authors
    • By Issue Date
    • Titles
  • This Collection
    • Authors
    • By Issue Date
    • Titles
  • Statistics

  • Most Popular Items
  • Statistics by Country
  • Most Popular Authors
  • Support

  • Contact us
  • FAQs
  • Admin login

  • Login
  • Segmentation and recognition of multi-model photo event

    Author(s)
    Yang, Feibin
    Huang, Qinghua
    Jin, Lianwen
    Liew, Alan Wee-Chung
    Griffith University Author(s)
    Liew, Alan Wee-Chung
    Year published
    2016
    Metadata
    Show full item record
    Abstract
    Photograph becomes more and more convenient with the rapid growing of smart phones, which makes photo become one of the most widely used social media. In this paper, we have proposed a segmentation and recognition method for multi-model photo event to help users organize and manage their increasing photo collections. Generative model of photo event is built by analyzing time, location, camera parameters and visual content of photos. Expectation-Maximization (EM) learning algorithm is applied to discover the best parameters of the proposed generative model. With the defined model, each photo is categorized into the corresponding ...
    View more >
    Photograph becomes more and more convenient with the rapid growing of smart phones, which makes photo become one of the most widely used social media. In this paper, we have proposed a segmentation and recognition method for multi-model photo event to help users organize and manage their increasing photo collections. Generative model of photo event is built by analyzing time, location, camera parameters and visual content of photos. Expectation-Maximization (EM) learning algorithm is applied to discover the best parameters of the proposed generative model. With the defined model, each photo is categorized into the corresponding event by calculating the maximum posteriori probability. The representativeness of an event is a photo collage constructed by selecting a set of representative photos from the corresponding event. The proposed method is characterized by the following properties: (1) unlike most of the existing photo event segmentation methods, the location of photos is treated as a key feature, (2) the representativeness of an event is a picture collage instead of a single photo, which is not only informative but also very appealing. The experimental results show that the proposed method is effective and efficient on the photo collections from three experienced smart phone users.
    View less >
    Journal Title
    Neurocomputing
    Volume
    172
    DOI
    https://doi.org/10.1016/j.neucom.2014.08.104
    Subject
    Engineering
    Psychology
    Publication URI
    http://hdl.handle.net/10072/142475
    Collection
    • Journal articles

    Footer

    Disclaimer

    • Privacy policy
    • Copyright matters
    • CRICOS Provider - 00233E
    • TEQSA: PRV12076

    Tagline

    • Gold Coast
    • Logan
    • Brisbane - Queensland, Australia
    First Peoples of Australia
    • Aboriginal
    • Torres Strait Islander