Ever-increasing amount of multimedia available online necessitates the development of new techniques and methods that can overcome the semantic gap problem. The said problem, encountered due to major disparities between inherent representational characteristics of multimedia and its semantic content sought by the user, has been a prominent research direction addressed by a great number of semantic augmentation approaches originating from such areas as machine learning, statistics, natural language processing, etc. In this paper, we review several of these recently developed techniques that bring together low-level representation of multimedia and its semantics in order to improve the efficiency of access and retrieval. We also present a distance-based discriminant analysis (DDA) method that defines the design of a basic building block classifier for distinguishing among a selected number of semantic categories. In addition to that, we demonstrate how a set of DDA classifiers can be grouped into a hierarchical ensemble for prediction of an arbitrary set of semantic classes.
['Serhiy Kosinov', 'Stéphane Marchand-Maillet']
Overview of approaches to semantic augmentation of multimedia databases for efficient access and content retrieval