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

Socpus ID

79951755954 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/79951755954

This document is currently not available here.

Share

COinS