Paper Info
Title
OLYBIA: ontology-based automatic image annotation system using semantic inference rules
Abstract
One of the big issues facing current content-based image retrieval is how to automatically extract the high-level concepts from images. In this paper, we present an efficient system that automatically extracts the high-level concepts from images by using ontologies and semantic inference rules. In our method, MPEG-7 visual descriptors are used to extract the visual features of image, and the visual features are mapped to semi-concepts via the mapping algorithm. We also build the visual and animal ontologies to bridge the semantic gap. The visual ontology allows the definition of relationships among the classes describing the visual features and has the values of semi-concepts as the property values. The animal ontology can be exploited to identify the highlevel concept in an image. Also, the semantic inference rules are applied to the ontologies to extract the high-level concept. Finally, we evaluate the proposed system using the image data set including various animal objects and discuss the limitations of our system.
Year
DOI
Venue
2007
10.1007/978-3-540-71703-4_42
DASFAA
Keywords
Field
DocType
semantic inference rule,current content-based image retrieval,image data,efficient system,proposed system,visual ontology,high-level concept,visual feature,ontology-based automatic image annotation,mpeg-7 visual descriptors,animal ontology,semantic gap,automatic image annotation,inference rule
Ontology (information science),Data mining,Ontology,Automatic image annotation,Information retrieval,Computer science,Semantic gap,Image retrieval,Mapping algorithm,Rule of inference,Visual Word
Conference
Volume
ISSN
Citations 
4443
0302-9743
9
PageRank 
References 
Authors
0.60
13
3
Name
Order
Citations
PageRank
Kyungwook Park1304.59
Jin-Woo Jeong29212.60
Dong-Ho Lee3799.96