Journal article
Content-Based Image Retrieval: A Comparison between Query by Example and Image Browsing Map Approaches
Journal of information science, v 30(3), pp 254-267
Jun 2004
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Efficient and effective retrieval techniques of images are desired because of the explosive growth of digital images. Content-based image retrieval is a promising approach because of its automatic indexing and retrieval based on their semantic features and visual appearance. The similarity of images depends on the feature representation and feature dissimilarity function. However, users have difficulties in representing their information needs in queries to content-based image retrieval systems. In this paper, we investigate two approaches, query by example and image browsing map. Activities to support the information seeking behavior are analyzed. The performance of these approaches is measured by a user evaluation. It is found that the image browsing map provides more functionalities and capabilities to support the features of information seeking behavior and produces better performance in searching images.
Metrics
Details
- Title
- Content-Based Image Retrieval: A Comparison between Query by Example and Image Browsing Map Approaches
- Creators
- Christopher C Yang - Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong
- Publication Details
- Journal of information science, v 30(3), pp 254-267
- Publisher
- Sage Publications; Thousand Oaks, CA
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000222619700007
- Scopus ID
- 2-s2.0-3142645162
- Other Identifier
- 991014877910804721
UN Sustainable Development Goals (SDGs)
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Source: SDGs in the Output
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Information Systems
- Information Science & Library Science