Conference paper
A nonparametric discriminant approach in resolving complex multi-class query for content-based image retrieval
IEEE
TENCON 2005 IEEE Region 10 Conference (Melbourne, Victoria, 21/11/2007–24/11/2007)
2005
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.
Details
- Title
- A nonparametric discriminant approach in resolving complex multi-class query for content-based image retrieval
- Authors/Creators
- K.P. Chung (Author/Creator) - Student Member, IEEEC.C. Fung (Author/Creator) - Member, IEEE
- Conference
- TENCON 2005 IEEE Region 10 Conference (Melbourne, Victoria, 21/11/2007–24/11/2007)
- Publisher
- IEEE
- Identifiers
- 991005542693207891
- Copyright
- © 2005 IEEE
- Murdoch Affiliation
- School of Information Technology
- Language
- English
- Resource Type
- Conference paper
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