Why one example is not enough for an image query

For over a decade, query-by-one-example has been a popular query paradigm for multimedia information retrieval. In this paper, we show through analyzing feature-to-semantics mapping that such a paradigm cannot realistically lead to scalable, satisfactory query performance. More specifically, we cluster a small image dataset based on the images' perceptual features, and show that these image clusters are not coherent to the semantic categories of the images. Though some image categories are well separated from the others in the input space formed by the perceptual features, most categories are co-located in more than one cluster. For a query-concept that is mixed with others in a number of clusters, the query-by-one-example paradigm simply lacks of information to clearly identify the target query concept, and hence cannot achieve satisfactory query results.