A nonparametric discriminant approach in resolving complex multi-class query for content-based image retrieval
Chung, K.P. and Fung, C.C. (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.
|PDF - Published Version |
Download (197kB) | Preview
*Subscription may be required
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.
|Publication Type:||Conference Paper|
|Murdoch Affiliation:||School of Information Technology|
|Copyright:||© 2005 IEEE|
|Item Control Page|