Fast Re-Ranking Of Visual Search Results By Example Selection

In this paper we present a simple, novel method to use state-of-the-art image concept detectors and publicly available image search engines to retrieve images for semantically more complex queries from local databases without re-indexing of the database. Our low-key, data-driven method for associative recognition of unknown, or more elaborate, concepts in images allows user selection of visual examples to tailor query results to the typical preferences of the user. The method is compared with a baseline approach using ConceptNet-based semantic expansion of the query phrase to known concepts, as set by the concepts of the image concept detectors. Using the output of the image concept detector as index for all images in the local image database, a quick nearest-neighbor matching scheme is presented that can match queries swiftly via concept output vectors. We show preliminary results for a number of query phrases followed by a general discussion.