6533b823fe1ef96bd127eab6
RESEARCH PRODUCT
Randomized Rx For Target Detection
José Antonio PadrónGustau Camps-vallsFatih NarAdrian Perez-suaysubject
FOS: Computer and information sciencesHyperparameter020301 aerospace & aeronauticsComputer Science - Machine LearningComputer scienceComputer Vision and Pattern Recognition (cs.CV)0211 other engineering and technologiesComputer Science - Computer Vision and Pattern RecognitionMultivariate normal distribution02 engineering and technologyObject detectionMachine Learning (cs.LG)symbols.namesakeKernel (linear algebra)Kernel method0203 mechanical engineeringKernel (statistics)Gaussian functionsymbolsClutterAnomaly detectionAlgorithm021101 geological & geomatics engineeringdescription
This work tackles the target detection problem through the well-known global RX method. The RX method models the clutter as a multivariate Gaussian distribution, and has been extended to nonlinear distributions using kernel methods. While the kernel RX can cope with complex clutters, it requires a considerable amount of computational resources as the number of clutter pixels gets larger. Here we propose random Fourier features to approximate the Gaussian kernel in kernel RX and consequently our development keep the accuracy of the nonlinearity while reducing the computational cost which is now controlled by an hyperparameter. Results over both synthetic and real-world image target detection problems show space and time efficiency of the proposed method while providing high detection performance.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2018-01-01 |