Title
A Lazy Processing Approach To User Relevance Feedback For Content-Based Image Retrieval
Keywords
Content-based image retrieval; Machine learning; Relevance feedback
Abstract
User Relevance feedback techniques based on learning methods such as Artificial Neural Networks and kernel machines have been widely used in content-based image retrieval. However, the traditional relevance feedback framework for existing techniques still suffers from: (1) high learning cost incurs substantial delay in responding to user relevance feedback; (2) the classifiers may be biased when the negative feedback samples out-number the positive feedback samples; and (3) The high feature dimensions compared to the size of the training set causes over fitting. We propose a new relevance feedback approach based on a lazy processing framework. This approach combines random sampling, data clustering, and ensembles of local classifiers to address the aforementioned problems. Our experimental studies show that the proposed framework provides a responsive user feedback environment that is capable of outperforming the traditional approach. © 2010 IEEE.
Publication Date
12-1-2010
Publication Title
Proceedings - 2010 IEEE International Symposium on Multimedia, ISM 2010
Number of Pages
342-346
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1109/ISM.2010.58
Copyright Status
Unknown
Socpus ID
79951755954 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/79951755954
STARS Citation
Nilpanich, Sirikunya; Hua, Kien A.; Petkova, Antoniya; and Ho, Yao H., "A Lazy Processing Approach To User Relevance Feedback For Content-Based Image Retrieval" (2010). Scopus Export 2010-2014. 500.
https://stars.library.ucf.edu/scopus2010/500