Capturing contextual relationship for effective media search
One of the central problems regarding media search is the semantic gap between the low-level features computed automatically from media data and the human interpretation of them. This is because the notion of similarity is usually based on high-level abstraction but the low-level features do not sometimes reflect the human perception. In this paper, we assume the semantics of media is determined by the contextual relationship in a dataset, and introduce the method to capture the contextual information from a large media (especially image) dataset for effective search. Similarity search in an image database based on this contextual information shows encouraging experimental results.
contextual relationship media search semantic gap similarity search