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Local Distance Functions: A Taxonomy, New Algorithms, and an Evaluation

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2 Author(s)
Ramanan, D. ; Univ. of California Irvine, Irvine, CA, USA ; Baker, S.

We present a taxonomy for local distance functions where most existing algorithms can be regarded as approximations of the geodesic distance defined by a metric tensor. We categorize existing algorithms by how, where, and when they estimate the metric tensor. We also extend the taxonomy along each axis. How: We introduce hybrid algorithms that use a combination of techniques to ameliorate overfitting. Where: We present an exact polynomial-time algorithm to integrate the metric tensor along the lines between the test and training points under the assumption that the metric tensor is piecewise constant. When: We propose an interpolation algorithm where the metric tensor is sampled at a number of references points during the offline phase. The reference points are then interpolated during the online classification phase. We also present a comprehensive evaluation on tasks in face recognition, object recognition, and digit recognition.

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:33 ,  Issue: 4 )
Biometrics Compendium, IEEE

Date of Publication:

April 2011

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