Building semantic hierarchies faithful to image semantics

Abstract

This paper proposes a new image-semantic measure, named "Semantico-Visual Relatedness of Concepts" (SVRC), to estimate the semantic similarity between concepts. The proposed measure incorporates visual, conceptual and contextual information to provide a measure which is more meaningful and more representative of image semantics. We also propose a new methodology to automatically build a semantic hierarchy suitable for the purpose of image annotation and/or classification. The building is based on the previously proposed measure SVRC and on a new heuristic, named TRUST-ME, to connect concepts with higher relatedness till the building of the final hierarchy. The built hierarchy explicitly encodes a general to specific concepts relationship and therefore provides a semantic structure to concepts which facilitates the semantic interpretation of images. Our experiments showed that the use of the constructed semantic hierarchies as a hierarchical classification framework provides a better image annotation. © 2012 Springer-Verlag.