A conceptual indexing method for content-based retrieval

A great deal of work has been done to define index structures to support feature-based similarity queries. However, other kinds of content-based retrieval, namely keyword-based and concept-based, are founded on different properties of the data space, which make these methods ineffective. Nevertheless, similarity notions are still needed, in order to manage incompleteness and imprecision in the representation of multimedia data, as well as in user query specification. In the paper, we present an indexing method which is based on partitioning the data space. We introduce the binary counterpart of the notions of minimum volume and minimum overlap, and combine them in a global hierarchical clustering criterion. We also show how the index structure induced by the clusterization can be exploited to deal with incompleteness and imprecision expressed in terms of answer precision and recall