- Cost-sensitive learning in social image tagging: review, new ideas and evaluation
- International Journal of Multimedia Information Retrieval
- Volume | Issue number
- 1 | 4
- Pages (from-to)
- Document type
- Faculty of Science (FNWI)
- Informatics Institute (IVI)
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.
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