Content-based image visualization

Chaomei Chen, George Gagaudakis, Paul Rosin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Citations (Scopus)


The proliferation of content-based image retrieval techniques has highlighted the need to understand the relationship between image clustering based on low-level image features and image clustering made by human users. In conventional image retrieval systems, images are typically characterized by a range of features such as color, texture, and shape. However, little is known to what extent these low-level features can be effectively combined with information visualization techniques such that users may explore images in a digital libraty according to visual similarities. In this article, we compared and analyzed a number of Pathfinder networks of images generated based on such features. Salient structures of images are visualized according to features extracted from color, texture, and shape orientation. Implications for visualizing and constructing hypermedia systems are discussed.

Original languageEnglish
Title of host publicationProceedings - IEEE International Conference on Information Visualisation, IV 2000
EditorsEbad Banissi, Mark W. McK. Bannatyne, Chaomei Chen, Farzad Khosrowshahi, Muhammad Sarfraz, Anna Ursyn
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)0769507433
Publication statusPublished - 2000
Event4th IEEE International Conference on Information Visualisation, IV 2000 - London, United Kingdom
Duration: 2000 Jul 192000 Jul 21

Publication series

NameProceedings of the International Conference on Information Visualisation
ISSN (Print)1093-9547


Conference4th IEEE International Conference on Information Visualisation, IV 2000
Country/TerritoryUnited Kingdom

Bibliographical note

Publisher Copyright:
© 2000 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition


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