Cost-Sensitive Learning in Social Image Tagging: Review, New Ideas and Evaluation Z. Li, M. S. Lew In International Journal of Multimedia Information Retrieval 2012. [bibtex] [pdf] [url]

Visual concept learning typically requires a set of expert labeled, manual training images. However, acquiring a sufficient number of reliable annotations can be time-consuming or impractical. Therefore, in many situations it is preferable to perform unsupervised learning on user contributed tags from abundant sources such as social Internet communities and websites. Cost-sensitive learning is a natural approach toward unsupervised visual concept learning because it fundamentally optimizes the learning system accuracy regarding the cost of an error. This paper reviews the problem of cost-sensitive unsupervised learning of visual concepts from social images, presents the new ideas, and gives a comparative evaluation of representative approaches from the research literature.

@Article{LiIJMIR2012,
  author       = "Li, Z. and Lew, M. S.",
  title        = "Cost-Sensitive Learning in Social Image Tagging: Review, New Ideas and Evaluation",
  journal      = "International Journal of Multimedia Information Retrieval",
  number       = "4",
  volume       = "1",
  pages        = "205--222",
  year         = "2012",
  url          = "https://ivi.fnwi.uva.nl/isis/publications/2012/LiIJMIR2012",
  pdf          = "https://ivi.fnwi.uva.nl/isis/publications/2012/LiIJMIR2012/LiIJMIR2012.pdf"
}