A Semantic Vector Space for Query by Image Example

image retrieval enables the user to search a database for visually similar images. In these scenarios, the user submits an example that is compared to the images in the database by their low-level characteristics such as colour, texture and shape. While visual similarity is essential for a vast number of applications, there are cases where a user needs to search for semantically similar images. For example, the user might want to find all images depicting bears on a river. This might be quite difficult using only low-level features, but using concept detectors for "bear" and "river" will produce results that are semantically closer to what the user requested. Following this idea, this paper studies a novel paradigm: query by semantic multimedia example. In this setting the user's query is processed at a semantic level: a vector of concept probabilities is inferred for each image and a similarity metric computes the distance between the concept vector of the query and of the concept vectors of the images in database. The system is evaluated with a COREL Stock Photo collection.