Pattern Recognition by Cluster Accumulation

摘要

When objects in images are small or blurred enough, geometric features are inadequate for reliable pattern recognition. We introduce the Pattern Recognition by Cluster Accumulation (PRCA) method to show that pattern recognition performance can be improved in this situation by using radiometric features for object detection. In addition, PRCA uses clustering to provide feature selection and dimensionality reduction. It uses accumulation to provide robustness against translation, rotation, cluster shape distortion, and inappropriate splitting or merging of clusters. We find that PRCA performs faster than normalized cross correlation and faster than mutual information methods.