Application of Meta-learning Principles in Multimedia Indexing

Abstract

Databases of video content traditionally rely on annotations and meta-data imported by a person, usually the uploader. This is supposedly due to a lack of an universal approach to the automated multimedia content annotation. As it may be hard or impossible to find a single classifier for all encountered combinations of different modalities or even a network of the classifiers, current interest of our research is to use meta-learning for multiple stages of the multimedia content classification. With this, we hope to handle correctly all modalities involved including their overlaps. Successively, the extracted classes will be used to build the index and later used for searching and discovery in the multimedia.