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.
|Title of host publication||Proceedings - IEEE International Conference on Information Visualisation, IV 2000|
|Editors||Ebad Banissi, Mark W. McK. Bannatyne, Chaomei Chen, Farzad Khosrowshahi, Muhammad Sarfraz, Anna Ursyn|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|Publication status||Published - 2000|
|Event||4th IEEE International Conference on Information Visualisation, IV 2000 - London, United Kingdom|
Duration: 2000 Jul 19 → 2000 Jul 21
|Name||Proceedings of the International Conference on Information Visualisation|
|Conference||4th IEEE International Conference on Information Visualisation, IV 2000|
|Period||00/7/19 → 00/7/21|
Bibliographical noteFunding Information:
This study was in part supported by the British research council EPSRC (GWL61088 and GRL94628).
© 2000 IEEE.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Computer Vision and Pattern Recognition