Facing scalability: Naming faces in an online social network
Pattern Recognition
Computers in Biology and Medicine
Perceptual relativity-based local hyperplane classification
Neurocomputing
Spatial measures between human poses for classification and understanding
AMDO'12 Proceedings of the 7th international conference on Articulated Motion and Deformable Objects
Measuring image distances via embedding in a semantic manifold
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Recognizing activities in multiple views with fusion of frame judgments
Image and Vision Computing
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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.