Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/40445
Title: Image annotation with relevance feedback using a semi-supervised and hierarchical approach
Authors: Chiang, C.-C.
Hung, M.-W.
Hung, Y.-P.
Leow, W.K. 
Keywords: Hierarchical classifier
Image annotation
Relevance feedback
Semi-supervised learning
Issue Date: 2008
Source: Chiang, C.-C.,Hung, M.-W.,Hung, Y.-P.,Leow, W.K. (2008). Image annotation with relevance feedback using a semi-supervised and hierarchical approach. VISAPP 2008 - 3rd International Conference on Computer Vision Theory and Applications, Proceedings 2 : 173-178. ScholarBank@NUS Repository.
Abstract: This paper presents an approach for image annotation with relevance feedback that interactively employs a semi-supervised learning to build hierarchical classifiers associated with annotation labels. We construct individual hierarchical classifiers each corresponding to one semantic label that is used for describing the semantic contents of the images. We adopt hierarchical approach for classifiers to divide the whole semantic concept associated with a label into several parts such that the complex contents in images can be simplified. We also design a semi-supervised approach for learning classifiers reduces the need of training images by use of both labeled and unlabeled images. This proposed semi-supervised and hierarchical approach is involved in an interactive scheme of relevance feedbacks to assist the user in annotating images. Finally, we describe some experiments to show the performance of the proposed approach.
Source Title: VISAPP 2008 - 3rd International Conference on Computer Vision Theory and Applications, Proceedings
URI: http://scholarbank.nus.edu.sg/handle/10635/40445
ISBN: 9789898111210
Appears in Collections:Staff Publications

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