Pattern Recognition Letters - Special issue on machine learning and data mining in pattern recognition
Information Retrieval
A Factor Graph Framework for Semantic Indexing and Retrieval in Video
CBAIVL '00 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'00)
Introduction to MPEG-7: Multimedia Content Description Interface
Introduction to MPEG-7: Multimedia Content Description Interface
Ontology-Based Semantic Indexing for MPEG-7 and TV-Anytime Audiovisual Content
Multimedia Tools and Applications
Evaluating the application of semantic inferencing rules to image annotation
Proceedings of the 3rd international conference on Knowledge capture
Building a visual ontology for video retrieval
Proceedings of the 13th annual ACM international conference on Multimedia
Learning the semantics of multimedia queries and concepts from a small number of examples
Proceedings of the 13th annual ACM international conference on Multimedia
Prototype-based classification
Applied Intelligence
A framework to enable the semantic inferencing and querying of multimedia content
International Journal of Web Engineering and Technology
Case-Based Reasoning on Images and Signals
Case-Based Reasoning on Images and Signals
Data mining on multimedia data
Data mining on multimedia data
Semantic annotation of images and videos for multimedia analysis
ESWC'05 Proceedings of the Second European conference on The Semantic Web: research and Applications
Brain volumes characterisation using hierarchical neural networks
Artificial Intelligence in Medicine
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Detailed, consistent semantic annotation of large collections of multimedia data is difficult and time-consuming. In domains such as eScience, digital curation and industrial monitoring, fine-grained high-quality labeling of regions enables advanced semantic querying, analysis and aggregation and supports collaborative research. Manual annotation is inefficient and too subjective to be a viable solution. Automatic solutions are often highly domain or application specific, require large volumes of annotated training corpi and, if using a 'black box' approach, add little to the overall scientific knowledge. This article evaluates the use of simple artificial neural networks to semantically annotate micrographs and discusses the generic process chain necessary for semi-automatic semantic annotation of images.