Abstract
Search algorithms in image databases usually return k nearest neighbours (kNN) of an image according to a similarity measure. This approach presents some anomalies and is based on assumptions that are not always satisfied. We have examined the causes of these anomalies and we have concluded that image query models have to exploit topological properties rather than the similarity degree. This paper proposes a topological model based on neighbourhood graphs built on automatically extracted image features. Each image is represented as a feature vector in R p and stands for a node in the neighbourhood graph. The graph exploration corresponds to database browsing, the neighbours of a node represent similar images. In order to perform query by example, the query image is represented as a R p feature vector and inserted in the graph by locally updating the neighbourhood graph. The topology of an image database is more informative than a similarity measure usually applied in content based image retrieval, as proved by our experiments. A prototype of a visualization and query tool called Smart Image Query (SIQ) is also introduced.
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