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Learning Distance for Arbitrary Visual Features

Ong, E-J and Bowden, R (2006) Learning Distance for Arbitrary Visual Features In: BMVC 2006, 2006-09-04 - 2006-09-07, Edinburgh, UK.

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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).

Item Type: Conference or Workshop Item (Conference Poster)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
Ong, E-J
Bowden, R
Date : 2006
DOI : 10.5244/C.20.77
Contributors :
Additional Information : © The authors. Published by The British Machine Vision Association (BMVA)
Depositing User : Symplectic Elements
Date Deposited : 11 Jun 2012 15:00
Last Modified : 31 Oct 2017 14:34

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