Title
An In-Memory Relevance Feedback Technique For High-Performance Image Retrieval Systems
Keywords
Dimension reduction; In memory relevance feedback
Abstract
Content-based image retrieval with relevant feedback has been widely adopted as the query model of choice for improved effectiveness in image retrieval. The effectiveness of this solution, however, depends on the efficiency of the feedback mechanism. Current methods rely on searching the database, stored on disks, in each round of relevance feedback. This strategy incurs long delay making relevance feedback less friendly to the user, especially for very large databases. Thus, scalability is a limitation of existing solutions. In this paper, we propose an in-memory relevance feedback technique to substantially reduce the delay associated with feedback processing, and therefore improve system usability. Our new data-independent dimensionality-reduction technique is used to compress the metadata to build a small in-memory database to support relevance feedback operations with minimal disk accesses. We compare the performance of this approach with conventional relevance feedback techniques in terms of computation efficiency and retrieval accuracy. The results indicate that the new technique substantially reduces response time for user feedback while maintaining the quality of the retrieval. Copyright 2007 ACM.
Publication Date
12-14-2007
Publication Title
Proceedings of the 6th ACM International Conference on Image and Video Retrieval, CIVR 2007
Number of Pages
9-16
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1145/1282280.1282282
Copyright Status
Unknown
Socpus ID
36849068360 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/36849068360
STARS Citation
Yu, Ning; Vu, Khanh; and Hua, Kien A., "An In-Memory Relevance Feedback Technique For High-Performance Image Retrieval Systems" (2007). Scopus Export 2000s. 5928.
https://stars.library.ucf.edu/scopus2000/5928