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Quantized Kernel Learning for Feature Matching
Danfeng Qin · Xuanli Chen · Matthieu Guillaumin · Luc V Gool

Tue Dec 09 04:00 PM -- 08:59 PM (PST) @ Level 2, room 210D

Matching local visual features is a crucial problem in computer vision and its accuracy greatly depends on the choice of similarity measure. As it is generally very difficult to design by hand a similarity or a kernel perfectly adapted to the data of interest, learning it automatically with as few assumptions as possible is preferable. However, available techniques for kernel learning suffer from several limitations, such as restrictive parametrization or scalability. In this paper, we introduce a simple and flexible family of non-linear kernels which we refer to as Quantized Kernels (QK). QKs are arbitrary kernels in the index space of a data quantizer, i.e., piecewise constant similarities in the original feature space. Quantization allows to compress features and keep the learning tractable. As a result, we obtain state-of-the-art matching performance on a standard benchmark dataset with just a few bits to represent each feature dimension. QKs also have explicit non-linear, low-dimensional feature mappings that grant access to Euclidean geometry for uncompressed features.

Author Information

Danfeng Qin (Computer Vision Lab, ETH Zurich)
Xuanli Chen (TU Munich)
Matthieu Guillaumin (ETH Zurich)
Luc V Gool (Computer Vision Lab, ETH Zurich)

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