Paper Info
Title
Ontology-Based Semantic Web Image Retrieval by Utilizing Textual and Visual Annotations
Abstract
The goal of traditional visual or textual-based image retrieval is to satisfy user’s queries by associating the images and semantic concepts effectively. As a result, perceptual structures of images have attracted researchers’ attention in recent studies. However, few past studies have been made on achieving semantic image retrieval by using image annotation techniques. To catch user’s ontological intention, we propose a new approach, namely Intelligent Web Image FetchER (iWIFER), which simultaneously considers the ontological requirements in usability, intelligence and effectiveness. Based on the proposed visual and textual-based annotation models, the image query becomes easy and effective. Through empirical evaluations, our annotation models can deliver accurate results for semantic web image retrieval.
Year
DOI
Venue
2009
10.1109/WI-IAT.2009.317
Web Intelligence/IAT Workshops
Keywords
Field
DocType
semantic image retrieval,ontological requirement,ontological intention,textual-based image retrieval,semantic web image retrieval,ontology-based semantic web image,visual annotations,image query,textual-based annotation model,annotation model,image annotation technique,semantic concept,utilizing textual,ontologies,computer science,search engines,image annotation,web pages,ontology,information retrieval,satisfiability,intelligent agent,usability,semantic web,image retrieval
Ontology (information science),Annotation,Automatic image annotation,Semantic Web Stack,Information retrieval,Web page,Computer science,Image retrieval,Semantic Web,Visual Word
Conference
Citations 
PageRank 
References 
1
0.35
6
Authors
4
Name
Order
Citations
PageRank
Ja-Hwung Su132924.53
Bo-Wen Wang2663.58
Hsin-Ho Yeh31337.26
Vincent S. Tseng42923161.33