A translation approach to portable ontology specifications
Knowledge Acquisition - Special issue: Current issues in knowledge modeling
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Context-Sensitive Semantic Query Expansion
ICAIS '02 Proceedings of the 2002 IEEE International Conference on Artificial Intelligence Systems (ICAIS'02)
Combining Top-Down and Bottom-Up Segmentation
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 4 - Volume 04
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Automatic image segmentation by integrating color-edge extraction and seeded region growing
IEEE Transactions on Image Processing
Region-based representations of image and video: segmentation tools for multimedia services
IEEE Transactions on Circuits and Systems for Video Technology
The MPEG-7 visual standard for content description-an overview
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
Semantics extraction from images
Knowledge-driven multimedia information extraction and ontology evolution
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In this paper we present a framework for simultaneous image segmentation and region labeling leading to automatic image annotation. The proposed framework operates at semantic level using possible semantic labels to make decisions on handling image regions instead of visual features used traditionally. In order to stress its independence of a specific image segmentation approach we applied our idea on two region growing algorithms, i.e. watershed and recursive shortest spanning tree. Additionally we exploit the notion of visual context by employing fuzzy algebra and ontological taxonomic knowledge representation, incorporating in this way global information and improving region interpretation. In this process, semantic region growing labeling results are being re-adjusted appropriately, utilizing contextual knowledge in the form of domain-specific semantic concepts and relations. The performance of the overall methodology is demonstrated on a real-life still image dataset from the popular domains of beach holidays and motorsports.