Title
Image Retrieval Based On User-Specified Features In Multi-Cluster Queries
Abstract
In a typical image retrieval system, all visual features of query images are used to determine image similarity. Thus, users are left to decide whether or not to include images that not only contain desirable features but also irrelevant ones. Fewer examples or a contaminated set of more could compromise the retrieval effectiveness of most similarity measures. In this paper, we extend our previous approach that allows users define queries by specifying relevant features present in image examples. The extended technique support queries decomposed in multiple clusters, each forming a subquery. Our experimental results have shown a remarkable improvement in retrieval performance. © 2006 IEEE.
Publication Date
12-1-2006
Publication Title
2006 IEEE International Conference on Multimedia and Expo, ICME 2006 - Proceedings
Volume
2006
Number of Pages
1769-1772
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1109/ICME.2006.262894
Copyright Status
Unknown
Socpus ID
34247580225 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/34247580225
STARS Citation
Vu, Khanh; Hua, Kien A.; and Koompairojn, Soontharee, "Image Retrieval Based On User-Specified Features In Multi-Cluster Queries" (2006). Scopus Export 2000s. 7735.
https://stars.library.ucf.edu/scopus2000/7735