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Kernel methods for classification is a well-studied area in which data are implicitly mapped from a lower-dimensional space to a higher dimensional space to improve classification accuracy. However, for most kernel methods, one must still choose a kernel to use for the problem. Since there is, in general, no way of knowing which kernel is the best, multiple kernel learning (MKL) is a technique used...
It is widely accepted that feature extraction is quite possibly the most critical step in computer vision. Typically, feature extraction is performed using a method such as the histogram of oriented gradients. In recent years, a shift has occurred from human to machine learned features, e.g., convolutional neural networks (CNNs) and Evolution-COnstructed (ECO) features. An advantage of our improved...
Kernel methods for classification is a well-studied area in which data are implicitly mapped from a lower-dimensional space to a higher-dimensional space to improve classification accuracy. However, for most kernel methods, one must still choose a kernel to use for the problem. Since there is, in general, no way of knowing which kernel is the best, multiple kernel learning (MKL) is a technique used...
Kernel methods for classification is a well-studied area in which data are implicitly mapped from a lower-dimensional space to a higher-dimensional space to improve classification accuracy. However, for most kernel methods, one must still choose a kernel to use for the problem. Since there is, in general, no way of knowing which kernel is the best, multiple kernel learning (MKL) is a technique used...
Remediation of the threat of explosive hazards is an extremely important goal. Such hazards are responsible for an unacceptable number of deaths and injuries to civilians as well as soldiers throughout the world. In this article, we put forth a new method for aggregating image space anomaly algorithm decisions across time (multi-look) as well as across disparate algorithms in Universal Transverse...
For reasons such as computational complexity, spatial and temporal information reduction, and human understandability, it is important that computer vision systems be equipped with the means to summarize their content in a natural language. Such rich descriptions are of use by both humans and computers for describing, recognizing, and tracking objects, activity, and their interactions at a desired...
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