Image retrieval based on user-specified features in queries with multiple examples

Many current image retrieval techniques allow queries to be defined with multiple examples from a presented set. In these systems, all visual features are extracted from these images and used to determine relevant images from the database. As a result, users are left to decide whether or not to include images that not only contain desirable features but also irrelevant ones. Fewer examples or a contaminated set of more either would compromise the retrieval effectiveness of most similarity measures. In this work, we examine this popular case when desired features present in image examples define the intent of the queries. We show how this consideration affects the selection of the representative query points and retrieval sets, and discuss the options whether or not to retrieve partially relevant images. Our experimental results have shown a remarkable improvement in retrieval performance.