High-dimensional entropy estimation for finite accuracy data: R-NN entropy estimator.
We address the problem of entropy estimation for high-dimensional finite-accuracy data. Our main application is evaluating high-order mutual information image similarity criteria for multimodal image registration. The basis of our method is an estimator based on k-th nearest neighbor (NN) distances, modified so that only distances greater than some constant R are evaluated. This modification requires a correction which is found numerically in a preprocessing step using quadratic programming. We compare experimentally our new method with k-NN and histogram estimators on synthetic data as well as for evaluation of mutual information for image similarity.
Kybic
Jan
J
Center for Machine Perception, Czech Technical University, Prague, Czech Republic. kybic@fel.cvut.cz
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D019277:Entropy; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity