Paper Title:
Automatic Image Tagging via a Generative Probabilistic Model
  Abstract

We present an approach to tag image automatically via visual topic detecting and initial annotations expanding. Visual topics are detected from corel5k dataset by probabilistic latent semantic analysis (PLSA) model. For an image which is to be tagged, PLSA is used to find visual topic of this image, and then construct initial annotations set. After initial annotations are generated, we use a weighted voting scheme and Flickr API to expand initial annotations. After the above two process, we combine initial annotations and expanded annotations together to construct final annotations. From experimental results, the conclusions can be draw that our PLSA based image tagging approach works effectively.

  Info
Periodical
Edited by
Ran Chen
Pages
3443-3447
DOI
10.4028/www.scientific.net/AMM.44-47.3443
Citation
Z. Liu, "Automatic Image Tagging via a Generative Probabilistic Model", Applied Mechanics and Materials, Vols. 44-47, pp. 3443-3447, 2011
Online since
December 2010
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