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|dc.title||Geometric optimum experimental design for collaborative image retrieval|
|dc.identifier.citation||Zhang, L., Wang, L., Lin, W., Yan, S. (2014-02). Geometric optimum experimental design for collaborative image retrieval. IEEE Transactions on Circuits and Systems for Video Technology 24 (2) : 346-359. ScholarBank@NUS Repository. https://doi.org/10.1109/TCSVT.2013.2276172|
|dc.description.abstract||Relevance feedback (RF) schemes have been widely designed to improve the performance of content-based image retrieval. Despite the success, it is not appropriate to require the user to label a large number of samples in RF. Collaborative image retrieval (CIR) aims to reduce the labeling efforts of the user by resorting to the auxiliary information. Support vector machine (SVM) active learning can select ambiguous samples as the most informative ones for the user to label with the help of the optimal hyperplane of SVM, and thus alleviate the labeling efforts of conventional RF. However, the optimal hyperplane of SVM is usually unstable and inaccurate with small-sized training data, and this is always the case in image retrieval since the user would not like to label a large number of feedback samples and cannot label each sample accurately all the time. In this paper, we propose a novel active learning method, i.e., geometric optimum experimental design (GOED), to select multiple representative samples in the database as the most informative ones for the user to label. Especially, GOED can alleviate the small-sized training data problem by leveraging the geometric structure of unlabeled samples in the reproducing kernel Hilbert space and thus further enhance the performance of image retrieval. Different from the conventional manifold regularization framework, the new method can effectively select the most informative samples for the user to label in image retrieval. By minimizing the expected average prediction variance on the test data, GOED has a clear geometric interpretation to select a set of the most representative samples in the database iteratively with the global optimum. Compared with the popular SVM active learning, our method is label-independent and can effectively avoid various potential problems caused by insufficient and inexactly labeled samples in RF, and is more appropriate and useful for image retrieval. Extensive experiments on both synthetic datasets and a real-world image database have been conducted to show the advantages of the proposed GOED for CIR. © 2013 IEEE.|
|dc.subject||content-based image retrieval (CBIR)|
|dc.subject||optimum experimental design|
|dc.subject||relevance feedback (RF)|
|dc.contributor.department||ELECTRICAL & COMPUTER ENGINEERING|
|dc.description.sourcetitle||IEEE Transactions on Circuits and Systems for Video Technology|
|Appears in Collections:||Staff Publications|
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