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A nonparametric discriminant approach in resolving complex multi-class query for content-based image retrieval

Chung, K.P. and Fung, C.C.ORCID: 0000-0001-5182-3558 (2005) A nonparametric discriminant approach in resolving complex multi-class query for content-based image retrieval. In: TENCON 2005 IEEE Region 10 Conference, 21-24 November 2007, Melbourne, Victoria pp. 1-4.

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Link to Published Version: http://dx.doi.org/10.1109/TENCON.2005.301240
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Abstract

Content-based image retrieval (CBIR) systems have drawn intense interest from many researchers in recent years. Over this period, certain degree of success has been achieved in domain-oriented systems for applications such as facial recognition and medical diagnosis. However, the machine learning techniques used in these systems mostly assume that all the targeted images belong to a single group. Thus, most of the research efforts so far have been trying to search for one or a combination of global image features that can be used to differentiate the targeted images from the rest. This is not the case for a generic image database. Quite often, images that are similar semantically may be completely different with the visual context. In this paper, the authors propose a local grouping strategy together with a multiple Gaussian distributions distance ranking approach in an attempt to address the retrieval and ranking of images that belong to multiple disjoint groups.

Item Type: Conference Paper
Murdoch Affiliation(s): School of Information Technology
Publisher: IEEE
Copyright: © 2005 IEEE
URI: http://researchrepository.murdoch.edu.au/id/eprint/994
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