Inferring semantics from textual information in multimedia retrieval

We propose a method for inferring semantic information from textual data in content-based multimedia retrieval. Training examples of images and videos belonging to a specific semantic class are associated with their low-level visual and aural descriptors augmented with textual features such as frequencies of significant words. A fuzzy mapping of a semantic class in the training set to a class of similar objects in the test set is created by using Self-Organizing Maps (SOMs) trained from the lowlevel descriptors. Experiments with two databases and different textual features show promising results, indicating the usefulness of the approach in bridging the gap from low-level visual features to