Abstract
Relevance feedback, as a user-in-the-loop mechanism, has been widely employed to improve the performance of content-based image retrieval. Generally, in a relevance feedback algorithm, two key components are: (1) how to select a subset of effective features from a large-scale feature pool and, (2) correspondingly, how to construct a suitable dissimilarity measure. In previous work, the biased discriminant analysis (BDA) has been proposed to address these two problems during the feedback iterations. However, BDA encounters the so called small samples size problem because it has a lack of training samples. In this paper, we utilize the generalized singular value decomposition (GSVD) to significantly reduce the small samples size problem in BDA. The developed algorithm is named GSVD for BDA (GBDA). We then kernelize the GBDA to nonlinear kernel feature space. A large amount of experiments were carried out upon a large scale database, which contains 17800 images. From the experimental results, GBDA and its kernelization are demonstrated to outperform the traditional BDA-based relevance feedback approaches and their kernel extensions, respectively.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
