Ontology-based multi-classification learning for video concept detection

Abstract

In this paper, an ontology-based multi-classification learning algorithm is adopted to detect concepts in the NIST TREC-2003 Video Retrieval Benchmark. NIST TREC-2003 Video Retrieval Benchmark defines 133 video concepts, organized hierarchically and each video data can belong to one or more concepts. Ontology-based multi-classification learning consists of two steps. At the first step, each single concept model is constructed independently. At the second step, ontology-based concept learning improves the accuracy of individual concept by considering the possible influence relations between concepts based on predefined ontology hierarchy. The advantage of ontology learning is that its influence path is based on ontology hierarchy, which has real semantic meanings. Besides semantics, ontology learning also considers the data correlation to decide the exact influence assigned to each path, which makes the influence more flexible according to data distribution. This learning algorithm can be used for multiple topic documents classification such as Internet documents and video documents. Based on NIST TREC video benchmark, we demonstrate that precision-recall can be significantly improved by taking ontology into account.