PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Descriptor Learning Using Convex Optimisation
K Simonyan, Andrea Vedaldi and Andrew Zisserman
In: ECCV 2012, 7-13 October 2012, Italy.

Abstract

The objective of this work is to learn descriptors suitable for the sparse feature detectors used in viewpoint invariant matching. We make a number of novel contributions towards this goal: first, it is shown that learning the pooling regions for the descriptor can be formulated as a convex optimisation problem selecting the regions using sparsity; second, it is shown that dimensionality reduction can also be formulated as a convex optimisation problem, using the nuclear norm to reduce dimensionality. Both of these problems use large margin discriminative learning methods. The third contribution is a new method of obtaining the positive and negative training data in a weakly supervised manner. And, finally, we employ a state-of-the-art stochastic optimizer that is efficient and well matched to the non-smooth cost functions proposed here. It is demonstrated that the new learning methods improve over the state of the art in descriptor learning for large scale matching, Brown et al. (PAMI 2011), and large scale object retrieval, Philbin et al. (ECCV 2010).

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:9594
Deposited By:Sunando Sengupta
Deposited On:19 October 2012