<title>Mining association rules between low-level image features and high-level concepts</title>

In image similarity retrieval systems, color is one of the most widely used features. Users who are not well versed with the image domain characteristics might be more comfortable in working with an Image Retrieval System that allows specification of a query in terms of keywords, thus eliminating the usual intimidation in dealing with very primitive features. In this paper we present two approaches to automatic image annotation, by finding those rules underlying the links between the low-level features and the high-level concepts associated with images. One scheme uses global color image information and classification tree based techniques. Through this supervised learning approach we are able to identify relationships between global color-based image features and some textual decriptors. In the second approach, using low-level image features that capture local color information and through a k-means based clustering mechanism, images are organized in clusters such that images that are similar are located in the same cluster. For each cluster, a set of rules is derived to capture the association between the localized color-based image features and the textual descriptors relevant to the cluster.