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Xiang-Jun Wang

dblp:40/5537 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2005
—ORCID · none

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

Graphics, computer vision, multimedia, augmented reality and games · 1Human-computer interaction and ubiquitous computing · 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 · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Multimedia analysis and retrieval
image annotation
0.112005
Semantic knowledge extraction and annotation for web images · ACM Multimedia 2005
Multimedia analysis and retrieval › image annotation
web image annotation
0.112005
Semantic knowledge extraction and annotation for web images · ACM Multimedia 2005

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

page layout analysis · 0.1
YearPublicationVenuePosition
2005 AIRE: an ambient interactive and responsive environment for mobile image management
abstract
This paper proposes an ambient interactive and responsive environment (AIRE) to improve user's image browsing experiences on the small-form-factor devices. This solution is characterized as two aspects: (1) establishing an ambient interactive communication across various devices; and (2) designing a distributed interface that crosses various devices to overcome the display constraint in mobile devices. In the AIRE system, a two-level image browsing scheme is designed to meet users' various image browsing needs.
Zhigang Hua, Xiang-Jun Wang, Xing Xie 0001, Qingshan Liu 0001, Hanqing Lu, Wei-Ying Ma
Mobile HCI2
2005 Semantic knowledge extraction and annotation for web images
abstract
Nowadays, images have become widely available on the World Wide Web (WWW). It's essential to develop effective ways for managing and retrieving such abundant images. Advantageously, compared to the traditional images where very little information is provided, the web images contain plentiful context data. This paper introduces a system that can automatically acquire semantic knowledge for web image annotation. By using a page layout analysis method that can precisely assign context to web images, we developed efficient algorithms to extract semantic knowledge for web images, such as description, people, temporal and geographic information. To validate the practicality and efficiency of this system, we applied it to about 6,500 images crawled from Web. Experiments demonstrated that our approach could achieve satisfactory results.
Zhigang Hua, Xiang-Jun Wang, Qingshan Liu 0001, Hanqing Lu
ACM Multimedia2