A Nonparametric Discriminant Approach in Resolving Complex Multi-class Query for Content-based Image Retrieval

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.