Selection of Concept Detectors for Video Search by Ontology-Enriched Semantic Spaces

This paper describes the construction and utilization of two novel semantic spaces, namely ontology-enriched semantic space (OSS) and ontology-enriched orthogonal semantic space (OS²), to facilitate the selection of concept detectors for video search. These two semantic spaces are enriched with ontology knowledge, while emphasizing consistent and uniform comparison of ontological relatedness among concepts for query-to-concept mapping. OS², in addition to being a linear space like OSS, also guarantees orthogonality of the semantic space. Compared with other ontology reasoning measures, both spaces are capable of providing platforms that offer a global view of concept inter-relatedness, by allowing evaluation of concept similarity in metric spaces. We simulate OSS and OS² by using LSCOM concepts and experiment search effectiveness with VIREO-374 concept detectors. Empirical observations indicate that the proposed semantic spaces enable more effective selection of concept detectors than eight other existing ontology measures. OS², in particular, is better in providing a viable and reasonable solution for fusion of multiple concept detectors.