Ontological Inference Framework with Joint Ontology Construction and Learning for Image Understanding

Abstract

Lack of human prior knowledge is one of the main reasons that semantic gap still remains when it comes to automatic multimedia understanding. In this work, we exploit the ontological structure of target concepts and propose an universal ontological inference framework for image understanding. The framework explicitly utilizes subclass and co-occurrence relation to effectively refine the coarse concept detections. Moreover, we show how to automatically construct and learn the underlying ontology required by the framework. As can be shown by experiments, the result is an effective and robust algorithm that characterizes well the structure of the target concepts and outperforms the state-of-the-art methods.