Learning Distances for Arbitrary Visual Features

This paper presents a method for learning distance functions of arbitrary feature representations that is based on the concept of wormholes. We introduce wormholes and describe how it provides a method for warping the topology of visual representation spaces such that a meaningful distance between examples is available. Additionally, we show how a more general distance function can be learnt through the combination of many wormholes via an inter-wormhole network. We then demonstrate the application of the distance learning method on a variety of problems including nonlinear synthetic data, face illumination detection and the retrieval of images containing natural landscapes and man-made objects (e.g. cities).