Gabor wavelet similarity maps for optimising hierarchical road sign classifiers

摘要

In recent years it has been shown that hierarchical classifiers have a significant advantage over single stage classifiers both in classification accuracy and in complexity of the classification features. This paper introduces a new method for creating the structure of hierarchical classifiers using a novel method for determining clusters. The proposed method uses features obtained using Gabor wavelets to create similarity maps, which help separating the class space into smaller more distinctive clusters. This approach has been applied on the Road Sign Recognition problem and has shown encouraging results in comparison to k-means algorithm.