Using Multimedia Ontologies for Automatic Image Annotation and Classification

In the era of big data, the use of formal models and techniques to represent and manage information is a necessary task to implement efficient intelligent information systems. In this paper we propose a complete framework to annotate and categorize images. Our approach is based on multimedia ontologies organized following a formal model to represent knowledge. Our ontologies use multimedia data and linguistic properties to bridge the gap between the target semantic classes and the available low-level multimedia descriptors. The multimedia features are automatically extracted using algorithms based on MPEG-7 standard. The informative image content is annotated with semantic information extracted from our ontologies and the categories are dynamically built by means of a general knowledge base. Experimental results show the efficiency of our approach in the annotation and classification tasks using a combination of textual and visual components.