Combining global and local information for knowledge-assisted image analysis and classification
EURASIP Journal on Advances in Signal Processing
Multi-Media Retrieval with Semantic Web: A Case Study in Airport Security Inspection Applications
Proceedings of the 2006 conference on Advances in Intelligent IT: Active Media Technology 2006
First steps to an audio ontology-based classifier for telemedicine
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Semantic annotation of images and videos for multimedia analysis
ESWC'05 Proceedings of the Second European conference on The Semantic Web: research and Applications
M-OntoMat-Annotizer: image annotation linking ontologies and multimedia low-level features
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Ontology-Based classifier for audio scenes in telemedicine
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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This paper presents a new object categorization method and shows how it can be used for image retrieval. Our approach involves machine learning and knowledge representation techniques. A major element of our approach is a visual concept ontology composed of several types of concepts (spatial concepts and relations, color concepts and texture concepts). Visual concepts contained in this ontology can be seen as an intermediate layer between domain knowledge and image processing procedures. Our approach is composed of three phases: (1) a knowledge acquisition phase, (2) a learning phase and (3) a categorization phase. This paper is mainly focused on phases (2) and (3). A major issue is the symbol grounding problem which consists of linking meaningfully symbols to sensory information. We propose a solution to this difficult issue by showing how learning techniques can map numerical features to visual concepts.