Paper Info
Title
SVG-to-RDF Image Semantization.
Abstract
The goal of this work is to provide an original (semi-automatic) annotation framework titled SVG-to-RDF which converts a collection of raw Scalable vector graphic (SVG) images into a searchable semantic-based RDF graph structure that encodes relevant features and contents. Using a dedicated knowledge base, SVG-to-RDF offers the user possible semantic annotations for each geometric object in the image, based on a combination of shape, color, and position similarity measures. Our method presents several advantages, namely i) achieving complete semantization of image content, ii) allowing semantic-based data search and processing using standard RDF technologies, iii) while being compliant with Web standards (i. e., SVG and RDF) in displaying images and annotation results in any standard Web browser, as well as iv) coping with different application domains. Our solution is of linear complexity in the size of the image and knowledge base structures used. Using our prototype SVG2RDF, several experiments have been conducted on a set of panoramic dental x-ray images to underline our approach's effectiveness, and its applicability to different application domains.
Year
DOI
Venue
2014
10.1007/978-3-319-11988-5_20
Lecture Notes in Computer Science
Keywords
DocType
Volume
Vector images,SVG,RDF,semantic graph,semantic processing,image annotation and retrieval,visual features,image feature similarity
Conference
8821
ISSN
Citations 
PageRank 
0302-9743
4
0.41
References 
Authors
19
3
Name
Order
Citations
PageRank
Khouloud Salameh151.52
Joe Tekli220420.30
Richard Chbeir369182.42