Which components are important for interactive image searching?
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AbstractWith many potential industrial applications, content-based image retrieval (CBIR) has recently gained more attention for image management and web searching. As an important too] 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.
All Author(s) ListTao DC, Tang XO, Li XL
Journal nameIEEE Transactions on Circuits and Systems for Video Technology
Volume Number18
Issue Number1
Pages3 - 11
LanguagesEnglish-United Kingdom
Keywordscontent-based image retrieval (CBIR); kernel machine; orthogonal complement component analysis (OCCA); relevance feedback (RF); support vector machine (SVM)
Web of Science Subject CategoriesEngineering; Engineering, Electrical & Electronic; ENGINEERING, ELECTRICAL & ELECTRONIC

Last updated on 2020-17-11 at 01:34