Using dually optimal LCA features in sensory and action spaces for classification.

摘要

Over years, a number of pattern recognition methods such as Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), sparse auto-encoders, k-means clustering, etc. have been studied in image matching based on global and local templates of image features. The Developmental Network (DN), which uses Lobe Component Analysis (LCA) features, has been applied to spatiotemporal event detection and recognition in complex, cluttered backgrounds. However, the DN method has not been compared to well-known major techniques in the pattern recognition community for global and local template based matching problems. In this work, the experiments fall into two categories---global template based object recognition and local template based scene classification. We apply the DN method to these problems and compare them to some widely used techniques in the pattern recognition community. The performance of the DN method is better or comparable to the global template based methods and comparable to some major local template based methods.