Video Object Segmentation Based on Feedback Schemes Guided by a Low-Level Scene Ontology

摘要

This paper presents a knowledge-based framework for video analysis which systematically exploits relationship among analysis stages. A set of step-by-step feedback paths controls feedback generation and reception between consecutive analysis stages. An analysis ontology, which includes occurrences in the scene from high to very low semantic level, controls iterative decisions on every stage. As a result, both overall and intermediate analysis results are improved. This paper presents the framework and focuses on its application to foreground objects extraction. Experimental results show that the framework provides a richer low-level representation of the scene and improved short-term change detection and foreground detection masks.