Deriving a Priori Co-occurrence Probability Estimates for Object Recognition from Social Networks and Text Processing

Certain components in images can be recognized with high accuracy, for example, backgrounds such as leaves, grass, snow, sky, water. These components provide the human eye with context for identifying items in the foreground. Likewise for the machine, the identification of background should help in the recognition of foreground objects. But, in this case, the computer needs explicit lists of object and background co-occurrence probabilities. We examine two ways of deriving estimates of these a priori object co-occurrence probabilities: using an online social network of people storing annotated images, FlickR; and using variations on co-occurrence frequencies in natural language text. We show that the object co-occurrence probabilities derived from both sources are very similar. The possibility of using non-image derived semantic knowledge drawn from text processing for object recognition opens up possibilities of mining a priori probabilities for a much wider class of objects than those found in manually annotated collections.