Regimvid at ImageCLEF 2015 Scalable Concept Image Annotation Task: Ontology based Hierarchical Image Annotation

In this paper, we describe our participation in the ImageCLEF 2015 Scalable Concept Image Annotation task. In this participation, we display our approach for an automatic image annotation by the use of an ontology-based semantic hierarchy handled at both learning and annotation steps. While recent works focused on the use of semantic hierarchies to improve concept detector accuracy, we are investigating the use of such hierarchies to reduce detector complexity and then, to handle efficiently large-scale image datasets. Our framework is based on two steps: (1) constructing a fuzzy ontology through analyzing learning dataset, and (2) guiding the annotation process through a reasoning engine. The obtained results confirm that this approach is promising for scalable image annotation.