Bifocal search: embedding context in local descriptors

Local descriptors are state-of-the-art of representing low-level visual information in object recognition. Because of their effectiveness, they are also largely used in content-based image retrieval whenever the query visually express a specific object to be retrieved between the images in the archive. Given that searching for the local descriptors can be very costly, many recent works have proposed to encode the local descriptors in a compact representation. In this paper, we propose to embed the aggregated information in the local descriptors in order to achieve higher effectiveness. The experimental results, obtained on a largely used public dataset, reveal the potential of the approach. Even if we only tested our approach in a content-based image retrieval scenario, the idea of combining aggregated and local information is general and could be applied in other similarity search tasks. We call the proposed approach bifocal searching because of the similarity with bifocal eyeglasses which have two parts with different focal lengths.