Cost-sensitive learning in social image tagging: review, new ideas and evaluation

Abstract

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.