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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
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    References listed on IDEAS

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    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.
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    Cited by:

    1. 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.
    2. Thouraya Bouabana-Tebibel & Stuart H. Rubin, 2016. "Towards common reusable semantics," Information Systems Frontiers, Springer, vol. 18(5), pages 819-823, October.
    3. 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.
    4. 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.

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