The choice of vantage objects for image retrieval

Suppose that we have a matrix of dissimilarities between n images of a database. For a new image, we would like to select the most similar image of our database. Because it may be too expensive to compute the dissimilarities for the new object to all images of our database, we want to find p?n “vantage objects” (Pattern Recognition 35 (2002) 69) from our database in order to select a matching image according to the least Euclidean distance between the vector of dissimilarities between the new image and the vantage objects and the corresponding vector for the images of the database. In this paper, we treat the choice of suitable vantage objects. We suggest a loss measure to assess the quality of a set of vantage objects: For every image, we select a matching image from the remaining images of the database by use of the vantage set, and we average the resulting dissimilarities. We compare two classes of choice strategies: The first one is based on a stepwise forward selection of vantage objects to optimize the loss measure. The second is to choose objects as representative as possible for the whole range of the database.