Knowledge-based image understanding systems: a survey
Computer Vision and Image Understanding
Using sharable ontology to retrieve historical images
Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries
SIGMA: A Knowledge-Based Aerial Image Understanding System
SIGMA: A Knowledge-Based Aerial Image Understanding System
Towards Computer Vision with Description Logics: Some Recent Progress
SPELMG '99 Proceedings of the Integration of Speech and Image Understanding
Qualitative Spatial Representation and Reasoning: An Overview
Fundamenta Informaticae - Qualitative Spatial Reasoning
Structured knowledge representation for image retrieval
Journal of Artificial Intelligence Research
Supporting knowledge-intensive inspection tasks with application ontologies
International Journal of Human-Computer Studies
Learning, detection and representation of multi-agent events in videos
Artificial Intelligence
Ontology based complex object recognition
Image and Vision Computing
Cognitive vision: The case for embodied perception
Image and Vision Computing
CASEE: a hierarchical event representation for the analysis of videos
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Video activity recognition in the real world
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Semantic integration of heterogeneous recognition systems
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
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Building knowledge bases for knowledge-based vision systems is a difficult task. This paper aims at showing how an ontology composed of visual concepts can be used as a guide for describing objects from a specific domain of interest. One of the most important benefits of our approach is that the knowledge acquisition process guided by the ontology leads to a knowledge base closer to low-level vision. A visual concept ontology and a dedicated knowledge acquisition tool have been developed and are also presented. We propose a generic methodology that is not linked to any application domain. Nevertheless, an example shows how the knowledge acquisition model can be applied to the description of pollen grain images. The use of an ontology for image description is the first step towards a complete cognitive vision system that will involve a learning layer.