Demonstrating the efficacy of polyadic queries

Similarity search is normally defined to be the task of identifying those objects, from a large collection, that are most similar to a further single object presented as a query. Using polyadic queries, a small set of objects are presented to the system, with the intent of finding those objects most similar to all elements of the query set. A few scenarios have previously demonstrated the usefulness of this notion. For example, we may be searching for images similar to a red balloon over a lake. With a single query, it is impossible to tell if the intent is to search for other images of balloons over lakes, or for other red balloons in any background. If instead we could present a system with a few different images of balloons, all of which are either all red, or all over lakes, the similarity search engine may be able to respond more appropriately. In this paper we demonstrate software which permits the user to provide explicit feedback by selecting the best few results from an intermediate set which are best suited to their original information need. A polyadic query can be formed from this set, which should give better results with a minimum of user interaction.