Classification of Vehicle Make by Combined Features and Random Subspace Ensemble

The identification of the make of vehicles is a challenge task. In this paper, we proposed to combine two different features, i.e., Pyramid Histogram of Oriented Gradients (PHOG) and Curve let transform, to describe vehicle images. The Curve let transform has the feature of higher time frequency resolution and high degree of directionality and anisotropy, which is particularly appropriate for images rich with edges. PHOG represents the local shape by a histogram of edge orientations computed for each image sub-region, quantized into a number of bins. Compared with previously proposed feature extraction approaches in vehicle recognition, PHOG has advantages in the extraction of discriminating information. A composite fetaure description from PHOG and Curve let Transform can further increase the accuracy of classification by taking their complementary information. We then investigate the applicability of the Random Subspace (RS) ensemble method for vehicle classification based on the combined features. A base classifier is trained with a randomly sampled subset of the original feature set and the ensemble assigns a class label by majority voting. Experimental results using more than 600 images from 21 makes show the effectiveness of the proposed approach. The composite feature is better than any single feature in the classification accuracy and the ensemble model produces better performance compared to any of the individual neural network base classifier. With moderate ensemble size 30, the Random Subspace ensembles offers a classification rate close to 96%, showing the promising potential in real applications.