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Automatic image annotation using semi-supervised generative modeling

Amiri, S. H ; Sharif University of Technology | 2015

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  1. Type of Document: Article
  2. DOI: 10.1016/j.patcog.2014.07.012
  3. Publisher: Elsevier Ltd , 2015
  4. Abstract:
  5. Image annotation approaches need an annotated dataset to learn a model for the relation between images and words. Unfortunately, preparing a labeled dataset is highly time consuming and expensive. In this work, we describe the development of an annotation system in semi-supervised learning framework which by incorporating unlabeled images into training phase reduces the system demand to labeled images. Our approach constructs a generative model for each semantic class in two main steps. First, based on Gamma distribution, a generative model is constructed for each semantic class using labeled images in that class. The second step incorporates the unlabeled images by using a modified EM algorithm to update parameters of the constructed generative models. Performance evaluation of the proposed method on a standard dataset reveals that using unlabeled images will result in considerable improvement in accuracy of the annotation systems when a limited number of labeled images for each semantic class are available
  6. Keywords:
  7. Generative modeling ; Automatic image annotation ; Gamma distribution ; Generative model ; Image annotation ; Semi-supervised ; Semi-supervised learning
  8. Source: Pattern Recognition ; Volume 48, Issue 1 , January , 2015 , Pages 174-188 ; 00313203 (ISSN)
  9. URL: http://www.sciencedirect.com/science/article/pii/S0031320314002714