Some later papers which were inspired by this work:
A new algorithm and systematic evaluation is presented for searching a database via relevance feedback. It represents a new image display strategy for the PicHunter system (Cox, 1996, 1997). The algorithm takes feedback in the form of relative judgments (``item A is more relevant than item B'') as opposed to the stronger assumption of categorical relevance judgments (``item A is relevant but item B is not''). It also exploits a learned probabilistic model of human behavior to make better use of the feedback it obtains. The algorithm can be viewed as an extension of indexing schemes like the k-d tree to a stochastic setting, hence the name ``stochastic-comparison search.'' In simulations, the amount of feedback required for the new algorithm scales like log(|D|), where |D| is the size of the database, while a simple query-by-example approach scales like |D|^a, where a < 1 depends on the structure of the database. This theoretical advantage is reflected by experiments with real users on a database of 1500 stock photographs.