1 research outputs found
Ontology-driven Event Type Classification in Images
Event classification can add valuable information for semantic search and the
increasingly important topic of fact validation in news. So far, only few
approaches address image classification for newsworthy event types such as
natural disasters, sports events, or elections. Previous work distinguishes
only between a limited number of event types and relies on rather small
datasets for training. In this paper, we present a novel ontology-driven
approach for the classification of event types in images. We leverage a large
number of real-world news events to pursue two objectives: First, we create an
ontology based on Wikidata comprising the majority of event types. Second, we
introduce a novel large-scale dataset that was acquired through Web crawling.
Several baselines are proposed including an ontology-driven learning approach
that aims to exploit structured information of a knowledge graph to learn
relevant event relations using deep neural networks. Experimental results on
existing as well as novel benchmark datasets demonstrate the superiority of the
proposed ontology-driven approach.Comment: Accepted for publication in: IEEE Winter Conference on Applications
of Computer Vision (WACV) 202