Nitish Barman - Academia.edu (original) (raw)
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Papers by Nitish Barman
Lecture Notes in Computer Science, 2003
The success of the Semantic Web hinges on being able to produce semantic markups on Web pages and... more The success of the Semantic Web hinges on being able to produce semantic markups on Web pages and their components, in a way that is cost-effective and consistent with adopted schemas and ontologies. Since images are an essential component of the Web, this work focuses on an intelligent approach to semantic annotation of images. We propose a three-layer architecture, in which the bottom layer organizes visual information extracted from the raw image contents, which are mapped to semantically meaningful keywords in the middle layer, which are then connected to schemas and ontologies on the top layer. Our key contribution is the use of machine learning algorithms for user-assisted, semi-automatic image annotation, in such a way that the knowledge of previously annotated images-both at metadata and visual levels-is used to speed up the annotation of subsequent images within the same domain (ontology) as well as to improve future query and retrieval of annotated images.
Lecture Notes in Computer Science, 2003
The success of the Semantic Web hinges on being able to produce semantic markups on Web pages and... more The success of the Semantic Web hinges on being able to produce semantic markups on Web pages and their components, in a way that is cost-effective and consistent with adopted schemas and ontologies. Since images are an essential component of the Web, this work focuses on an intelligent approach to semantic annotation of images. We propose a three-layer architecture, in which the bottom layer organizes visual information extracted from the raw image contents, which are mapped to semantically meaningful keywords in the middle layer, which are then connected to schemas and ontologies on the top layer. Our key contribution is the use of machine learning algorithms for user-assisted, semi-automatic image annotation, in such a way that the knowledge of previously annotated images-both at metadata and visual levels-is used to speed up the annotation of subsequent images within the same domain (ontology) as well as to improve future query and retrieval of annotated images.