Vehicle classification using sparse coding and spatial pyramid matching

This work adopts sparse coding and spatial pyramid matching to classify the vehicle images. The targets of interest, vehicles in the images, are always degraded in the complex circumstance. Hence, it seems difficult to carry out the classification task by the methods combined gray feature and traditional classifiers. Considering the vehicle image without assignment and complex influence caused by weather, this paper proposes a vehicle classification method based on sparse coding and spatial pyramid matching. First, the proposed method extracts a patch-based sparse feature computed with a discriminate dictionary. With dualizing the sparse feature, the spatial pyramid model is employed to generate a long but sparse feature. At last, SVM with the histogram intersection kernel finishes the ultimate classification task. Diverse from the traditional bag of features model employed to compute the histogram in each level of the spatial pyramid, this paper codes the image patch with a fine learned and discriminate dictionary for a better representation than the gradient-based feature extraction. Fast iteration method on computing the sparse feature ensures the real-time need. Experimental results on the vehicle datasets includes sedan, taxi, van, and truck show the efficiency and accuracy of the proposed method for vehicle classification in practice.