1,420 research outputs found
Kernel methods in machine learning
We review machine learning methods employing positive definite kernels. These
methods formulate learning and estimation problems in a reproducing kernel
Hilbert space (RKHS) of functions defined on the data domain, expanded in terms
of a kernel. Working in linear spaces of function has the benefit of
facilitating the construction and analysis of learning algorithms while at the
same time allowing large classes of functions. The latter include nonlinear
functions as well as functions defined on nonvectorial data. We cover a wide
range of methods, ranging from binary classifiers to sophisticated methods for
estimation with structured data.Comment: Published in at http://dx.doi.org/10.1214/009053607000000677 the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Remove Cosine Window from Correlation Filter-based Visual Trackers: When and How
Correlation filters (CFs) have been continuously advancing the
state-of-the-art tracking performance and have been extensively studied in the
recent few years. Most of the existing CF trackers adopt a cosine window to
spatially reweight base image to alleviate boundary discontinuity. However,
cosine window emphasizes more on the central region of base image and has the
risk of contaminating negative training samples during model learning. On the
other hand, spatial regularization deployed in many recent CF trackers plays a
similar role as cosine window by enforcing spatial penalty on CF coefficients.
Therefore, we in this paper investigate the feasibility to remove cosine window
from CF trackers with spatial regularization. When simply removing cosine
window, CF with spatial regularization still suffers from small degree of
boundary discontinuity. To tackle this issue, binary and Gaussian shaped mask
functions are further introduced for eliminating boundary discontinuity while
reweighting the estimation error of each training sample, and can be
incorporated with multiple CF trackers with spatial regularization. In
comparison to the counterparts with cosine window, our methods are effective in
handling boundary discontinuity and sample contamination, thereby benefiting
tracking performance. Extensive experiments on three benchmarks show that our
methods perform favorably against the state-of-the-art trackers using either
handcrafted or deep CNN features. The code is publicly available at
https://github.com/lifeng9472/Removing_cosine_window_from_CF_trackers.Comment: 13 pages, 7 figures, submitted to IEEE Transactions on Image
Processin
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