Semantic, Automatic Image Annotation Based On Multi-Layered Active Contours and Decision Trees

Olszewska, Joanna Isabelle (2013) Semantic, Automatic Image Annotation Based On Multi-Layered Active Contours and Decision Trees. International Journal of Advanced Computer Science and Applications, 4 (8). pp. 201-208. ISSN 2156-5570

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Abstract

In this paper, we propose a new approach for automatic image annotation (AIA) in order to automatically and efficiently assign linguistic concepts to visual data such as digital images, based on both numeric and semantic features. The presented method first computes multi-layered active contours. The first-layer active contour corresponds to the main object or foreground, while the next-layers active contours delineate the object’s subparts. Then, visual features are extracted within the regions segmented by these active contours and are mapped into semantic notions. Next, decision trees are trained based on these attributes, and the image is semantically annotated using the resulting decision rules. Experiments carried out on several standards datasets have demonstrated the reliability and the computational effectiveness of our AIA system.

Item Type:	Article
Contributors:	Contribution Name Email ORCID Author Olszewska, Joanna Isabelle j.olszewska@hud.ac.uk UNSPECIFIED
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Schools:	School of Computing and Engineering School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge
Related URLs:	Author Publisher
Depositing User:	Joanna Olszewska
Date Deposited:	13 Aug 2013 13:35
Last Modified:	28 Aug 2021 19:46
URI:	http://eprints.hud.ac.uk/id/eprint/18124

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