114,057 research outputs found
Online Metric-Weighted Linear Representations for Robust Visual Tracking
In this paper, we propose a visual tracker based on a metric-weighted linear
representation of appearance. In order to capture the interdependence of
different feature dimensions, we develop two online distance metric learning
methods using proximity comparison information and structured output learning.
The learned metric is then incorporated into a linear representation of
appearance.
We show that online distance metric learning significantly improves the
robustness of the tracker, especially on those sequences exhibiting drastic
appearance changes. In order to bound growth in the number of training samples,
we design a time-weighted reservoir sampling method.
Moreover, we enable our tracker to automatically perform object
identification during the process of object tracking, by introducing a
collection of static template samples belonging to several object classes of
interest. Object identification results for an entire video sequence are
achieved by systematically combining the tracking information and visual
recognition at each frame. Experimental results on challenging video sequences
demonstrate the effectiveness of the method for both inter-frame tracking and
object identification.Comment: 51 pages. Appearing in IEEE Transactions on Pattern Analysis and
Machine Intelligenc
-K-SVD: A Robust Dictionary Learning Algorithm With Simultaneous Update
We develop a dictionary learning algorithm by minimizing the
distortion metric on the data term, which is known to be robust for
non-Gaussian noise contamination. The proposed algorithm exploits the idea of
iterative minimization of weighted error. We refer to this algorithm
as -K-SVD, where the dictionary atoms and the corresponding sparse
coefficients are simultaneously updated to minimize the objective,
resulting in noise-robustness. We demonstrate through experiments that the
-K-SVD algorithm results in higher atom recovery rate compared with the
K-SVD and the robust dictionary learning (RDL) algorithm proposed by Lu et al.,
both in Gaussian and non-Gaussian noise conditions. We also show that, for
fixed values of sparsity, number of dictionary atoms, and data-dimension, the
-K-SVD algorithm outperforms the K-SVD and RDL algorithms when the
training set available is small. We apply the proposed algorithm for denoising
natural images corrupted by additive Gaussian and Laplacian noise. The images
denoised using -K-SVD are observed to have slightly higher peak
signal-to-noise ratio (PSNR) over K-SVD for Laplacian noise, but the
improvement in structural similarity index (SSIM) is significant (approximately
) for lower values of input PSNR, indicating the efficacy of the
metric
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