Knowledge-Assisted Image Analysis Based on Context and Spatial Optimization

In this article, an approach to semantic image analysis is presented. Under the proposed approach, ontologiesareusedtocapturegeneral,spatial,andcontextualknowledgeofadomain,andagenetic algorithm is applied to realize the final annotation. The employed domain knowledge considers high-level information in terms of the concepts of interest of the examined domain, contextual information in the form of fuzzy ontological relations, as well as low-level information in terms of prototypicallow-levelvisualdescriptors.Toaccountfortheinherentambiguityinvisualinformation, uncertainty has been introduced in the spatial relations definition. First, an initial hypothesis set of graded annotations is produced for each image region, and then context is exploited to update appropriately the estimated degrees of confidence. Finally, a genetic algorithm is applied to decide the most plausible annotation by utilizing the visual and the spatial concepts definitions included in the domain ontology. Experime...

• Type: Journal
• Year: 2006
• Where: IJSWIS
• Authors: Georgios Th. Papadopoulos, Phivos Mylonas, Vasileios Mezaris, Yannis S. Avrithis, Ioannis Kompatsiaris
• Keywords: Annotation, Fuzzy Ontological Relations, IJSWIS 2006, Semantic Image Analysis