Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/13029
Title: Which components are important for interactive image searching?
Authors: Tao, D
Tang, X
Li, X
Keywords: Content based image retrieval (CBIR)
Content-based image retrieval (CBIR)
Kernel machine
Orthogonal complement component analysis (OCCA)
Relevance feedback (RF)
Support vector machine (SVM)
Issue Date: 2008
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on circuits and systems for video technology, 2008, v. 18, no. 1, 4358679, p. 3-11 How to cite?
Journal: IEEE transactions on circuits and systems for video technology 
Abstract: With many potential industrial applications, content-based image retrieval (CBIR) has recently gained more attention for image management and web searching. As an important tool to capture users' preferences and thus to improve the performance of CBIR systems, a variety of relevance feedback (RF) schemes have been developed in recent years. One key issue in RF is: which features (or feature dimensions) can benefit this human-computer iteration procedure? In this paper, we make theoretical and practical comparisons between principal and complement components of image features in CBIR RF. Most of the previous RF approaches treat the positive and negative feedbacks equivalently although this assumption is not appropriate since the two groups of training feedbacks have very different properties. That is, all positive feedbacks share a homogeneous concept while negative feedbacks do not. We explore solutions to this important problem by proposing an orthogonal complement component analysis. Experimental results are reported on a real-world image collection to demonstrate that the proposed complement components method consistently outperforms the conventional principal components method in both linear and kernel spaces when users want to retrieve images with a homogeneous concept.
URI: http://hdl.handle.net/10397/13029
ISSN: 1051-8215
EISSN: 1558-2205
DOI: 10.1109/TCSVT.2007.906936
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

55
Last Week
0
Last month
0
Citations as of Sep 23, 2017

WEB OF SCIENCETM
Citations

43
Last Week
1
Last month
0
Citations as of Sep 21, 2017

Page view(s)

57
Last Week
2
Last month
Checked on Sep 17, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.