Traffic sign classification using two-layer image representation
Yingying Zhu, Xinggang Wang, Cong Yao, Xiang Bai · 2013
This paper makes use of locality-constrained linear coding (LLC) in a two-layer image representation framework for traffic sign recognition. As a multi-category classification problem with unbalanced frequencies and variations, many machine learning approaches have been adopted with some low level features for traffic sign recognition. To the best of our knowledge, this is the first method using coding features for traffic sign recognition. First, we extract features(dense SIFT features, HOG features and LBP features) and encode them with a k-means generated codebook and LLC. Second, each traffic sign image is represented by the features generated by spatial pyramid matching (SPM). Then, all the image representations from each kind of features are concatenated together as the final image representation. Finally, we show that a linear SVM classifier trained with this image representation can achieve the state-of-the-art recognition rate of 99.67% on the well-known German Traffic Sign Recognition Benchmark.