IDEAS home Printed from https://ideas.repec.org/a/spr/infosf/v18y2016i5d10.1007_s10796-016-9660-z.html
   My bibliography  Save this article

Weighted subspace modeling for semantic concept retrieval using gaussian mixture models

Author

Listed:
  • Chao Chen

    (University of Miami)

  • Mei-Ling Shyu

    (University of Miami)

  • Shu-Ching Chen

    (Florida International University)

Abstract

At the era of digital revolution, social media data are growing at an explosive speed. Thanks to the prevailing popularity of mobile devices with cheap costs and high resolutions as well as the ubiquitous Internet access provided by mobile carriers, Wi-Fi, etc., numerous numbers of videos and pictures are generated and uploaded to social media websites such as Facebook, Flickr, and Twitter everyday. To efficiently and effectively search and retrieve information from the large amounts of multimedia data (structured, semi-structured, or unstructured), lots of algorithms and tools have been developed. Among them, a variety of data mining and machine learning methods have been explored and proposed and have shown their effectiveness and potentials in handling the growing requests to retrieve semantic information from those large-scale multimedia data. However, it is well-acknowledged that the performance of such multimedia semantic information retrieval is far from satisfactory, due to the challenges like rare events, data imbalance, etc. In this paper, a novel weighted subspace modeling framework is proposed that is based on the Gaussian Mixture Model (GMM) and is able to effectively retrieve semantic concepts, even from the highly imbalanced datasets. Experimental results performed on two public-available benchmark datasets against our previous GMM-based subspace modeling method and the other prevailing counterparts demonstrate the effectiveness of the proposed weighted GMM-based subspace modeling framework with the improved retrieval performance in terms of the mean average precision (MAP) values.

Suggested Citation

  • Chao Chen & Mei-Ling Shyu & Shu-Ching Chen, 2016. "Weighted subspace modeling for semantic concept retrieval using gaussian mixture models," Information Systems Frontiers, Springer, vol. 18(5), pages 877-889, October.
  • Handle: RePEc:spr:infosf:v:18:y:2016:i:5:d:10.1007_s10796-016-9660-z
    DOI: 10.1007/s10796-016-9660-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10796-016-9660-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10796-016-9660-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lin Lin & Mei-Ling Shyu, 2010. "Weighted Association Rule Mining for Video Semantic Detection," International Journal of Multimedia Data Engineering and Management (IJMDEM), IGI Global, vol. 1(1), pages 37-54, January.
    2. Chao Chen & Tao Meng & Lin Lin, 2013. "A Web-Based Multimedia Retrieval System with MCA-Based Filtering and Subspace-Based Learning Algorithms," International Journal of Multimedia Data Engineering and Management (IJMDEM), IGI Global, vol. 4(2), pages 13-45, April.
    3. Hsin-Yu Ha & Fausto C. Fleites & Shu-Ching Chen, 2013. "Content-Based Multimedia Retrieval Using Feature Correlation Clustering and Fusion," International Journal of Multimedia Data Engineering and Management (IJMDEM), IGI Global, vol. 4(2), pages 46-64, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Claudia Diamantini & Paolo Lo Giudice & Domenico Potena & Emanuele Storti & Domenico Ursino, 2021. "An Approach to Extracting Topic-guided Views from the Sources of a Data Lake," Information Systems Frontiers, Springer, vol. 23(1), pages 243-262, February.
    2. Eric Golinko & Xingquan Zhu, 2019. "Generalized Feature Embedding for Supervised, Unsupervised, and Online Learning Tasks," Information Systems Frontiers, Springer, vol. 21(1), pages 125-142, February.
    3. Claudia Diamantini & Paolo Lo Giudice & Domenico Potena & Emanuele Storti & Domenico Ursino, 0. "An Approach to Extracting Topic-guided Views from the Sources of a Data Lake," Information Systems Frontiers, Springer, vol. 0, pages 1-20.
    4. Thouraya Bouabana-Tebibel & Stuart H. Rubin, 2016. "Towards common reusable semantics," Information Systems Frontiers, Springer, vol. 18(5), pages 819-823, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Haiman Tian & Shu-Ching Chen & Mei-Ling Shyu, 0. "Evolutionary Programming Based Deep Learning Feature Selection and Network Construction for Visual Data Classification," Information Systems Frontiers, Springer, vol. 0, pages 1-14.
    2. Haiman Tian & Shu-Ching Chen & Mei-Ling Shyu, 2020. "Evolutionary Programming Based Deep Learning Feature Selection and Network Construction for Visual Data Classification," Information Systems Frontiers, Springer, vol. 22(5), pages 1053-1066, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:infosf:v:18:y:2016:i:5:d:10.1007_s10796-016-9660-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.