Geometric discriminative deep features for traffic image analysis

Abstract

Traffic image analysis is an important application in intelligent transportation. For local features’ robustness to image variances, such as scale changes and occlusions, they are widely used in image classification. However, how to integrate these local features for modeling traffic images optimally is still a crucial challenge. In this paper, a novel deep learning method, geometric discriminative feature fusion (GDFF), is proposed to tackle this problem. First, we use a variety of data sets to train the general convolutional neural network (CNN), which is used to extract the features of the training and test set after deep level. Deep architecture makes it possible for people to learn more abstract and internal features that are robust to changes in viewpoint and illumination. It can fuse image geometric related local features, such as local regions’ RGB histograms, into high level discriminative features, which can be used for better classifying complex scene images. Our framework’s central task is to build a structural kernel, called discriminative topological kernel. Firstly, we segment the traffic images into several regions and use a region connected graph (RCG) to model regions location relationships. We use frequent sub graph mining algorithm to mine all frequent sub structures (topologies) occurs in all training RCGs. And a selection algorithm is designed to select the k qualified topologies from the entire mined frequent topologies. We call these selected topologies geometric feature fusers, which are both high discriminative and low redundant structures in all training RCGs. Finally, given a pair of RCGs and to each geometric fuser, we extract all pairs of sub graphs sharing the same topology and calculate distance between them. All k distances are accumulated for the final kernel. The experimental result demonstrates the effectiveness of our method.