Athletics images interpretation using structural ontology model

Abstract

The continual rapid growth in digital content acquisition and visualization makes it increasingly challenging to
find, organize, and access visual information. Typically, image classification and retrieval systems tend to rely
only on the lowlevel visual structure within images. Image classification methods usually perform based upon a
vector space model. This paper presents a framework to restructure the vector space model of visual words with
respect to a structural ontology model in order to resolve visual synonym and polysemy problems. The experimental
results show that our method can disambiguate visual word senses effectively and can significantly improve
classification, interpretation and retrieval performance for the athletics images.