290,255 research outputs found
Non-sparse Linear Representations for Visual Tracking with Online Reservoir Metric Learning
Most sparse linear representation-based trackers need to solve a
computationally expensive L1-regularized optimization problem. To address this
problem, we propose a visual tracker based on non-sparse linear
representations, which admit an efficient closed-form solution without
sacrificing accuracy. Moreover, in order to capture the correlation information
between different feature dimensions, we learn a Mahalanobis distance metric in
an online fashion and incorporate the learned metric into the optimization
problem for obtaining the linear representation. We show that online metric
learning using proximity comparison significantly improves the robustness of
the tracking, especially on those sequences exhibiting drastic appearance
changes. Furthermore, in order to prevent the unbounded growth in the number of
training samples for the metric learning, we design a time-weighted reservoir
sampling method to maintain and update limited-sized foreground and background
sample buffers for balancing sample diversity and adaptability. Experimental
results on challenging videos demonstrate the effectiveness and robustness of
the proposed tracker.Comment: Appearing in IEEE Conf. Computer Vision and Pattern Recognition, 201
LCOT: Linear circular optimal transport
The optimal transport problem for measures supported on non-Euclidean spaces
has recently gained ample interest in diverse applications involving
representation learning. In this paper, we focus on circular probability
measures, i.e., probability measures supported on the unit circle, and
introduce a new computationally efficient metric for these measures, denoted as
Linear Circular Optimal Transport (LCOT). The proposed metric comes with an
explicit linear embedding that allows one to apply Machine Learning (ML)
algorithms to the embedded measures and seamlessly modify the underlying metric
for the ML algorithm to LCOT. We show that the proposed metric is rooted in the
Circular Optimal Transport (COT) and can be considered the linearization of the
COT metric with respect to a fixed reference measure. We provide a theoretical
analysis of the proposed metric and derive the computational complexities for
pairwise comparison of circular probability measures. Lastly, through a set of
numerical experiments, we demonstrate the benefits of LCOT in learning
representations of circular measures
Learning Non-Metric Visual Similarity for Image Retrieval
Measuring visual similarity between two or more instances within a data distribution is a fundamental task in image retrieval. Theoretically, non-metric distances are able to generate a more complex and accurate similarity model than metric distances, provided that the non-linear data distribution is precisely captured by the system. In this work, we explore neural networks models for learning a non-metric similarity function for instance search. We argue that non-metric similarity functions based on neural networks can build a better model of human visual perception than standard metric distances. As our proposed similarity function is differentiable, we explore a real end-to-end trainable approach for image retrieval, i.e. we learn the weights from the input image pixels to the final similarity score. Experimental evaluation shows that non-metric similarity networks are able to learn visual similarities between images and improve performance on top of state-of-the-art image representations, boosting results in standard image retrieval datasets with respect standard metric distances
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