Compressive Distance Classifier Correlation Filter

Abstract

Compressed Sensing (CS) is seen as the pathway to increase the efficiency of sensor systems such as MRI, SAR and SAS while avoiding the huge costs and related processing accompanying high-resolution data acquisition. While there has been a surge in the number of sensor systems and related algorithms using CS, target/object recognition in the sensing domain which offers numerous advantages, is a rather nascent field. The state-of-the-art in this field includes the Smashed Filter (SF), which is a reduced dimensionality maximum likelihood classifier. Nevertheless, the accuracy of the filter remains low for practical applications, especially with variations in scale, translation and rotation in the test data. This paper offers a new type of filter -called the Compressive Distance Classifier Correlation Filter (CDCCF), which applies a transformation in the CS domain thereby increasing the distance between intraclass correlation peaks while reducing the distance between inter-class correlation peaks and is based on the Restricted Isometry Property (RIP) of the compressed manifold and the Johnson LindenstraussLemma. Results presented show that the accuracy of the CDCCF filter is about 70% on a 12 class test data set, which is over a two-fold increase in accuracy over the SF. Confusion matrices, measures of ROC, Mean Average Precision and Accuracy demonstrate the robust performance of the algorithm over SF across different compressive sampling resolutions.