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Shun-Wen Cho

dblp:23/2971 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2000
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Multimedia analysis and retrieval · 50% Image and video processing · 50%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Image and video processing › image segmentation
color image segmentation		 0.012000
Hierarchical color image region segmentation for content-based image retrieval system · IEEE Trans. Image Process. 2000
Multimedia analysis and retrieval › image retrieval
content-based image retrieval		 0.012000
Hierarchical color image region segmentation for content-based image retrieval system · IEEE Trans. Image Process. 2000
Image and video processing
image segmentation		 0.012000
Hierarchical color image region segmentation for content-based image retrieval system · IEEE Trans. Image Process. 2000
Multimedia analysis and retrieval › image retrieval › content-based image retrieval
region-based image retrieval		 0.012000
Hierarchical color image region segmentation for content-based image retrieval system · IEEE Trans. Image Process. 2000

Methods — techniques the papers use, named apart from their topics

relationship tree matching · 0.0
YearPublicationVenuePosition
2000 Hierarchical color image region segmentation for content-based image retrieval system
abstract
In this work, we propose a model of a content-based image retrieval system by using the new idea of combining a color segmentation with relationship trees and a corresponding tree-matching method. We retain the hierarchical relationship of the regions in an image during segmentation. Using the information of the relationships and features of the regions, we can represent the desired objects in images more accurately. In retrieval, we compare not only region features but also region relationships.
Chiou-Shann Fuh, Shun-Wen Cho, Kai Essig
 IEEE Trans. Image Process.2