Binary coding-based vehicle image classification

Vehicle image classification can describe the visual vehicle with a semantically meaningful category directly. Motivated by its importance, this paper proposes a fast vehicle image classification based on binary coding. As for the vehicle image classification, this paper focuses on the image obtained from the video via analyzing the moving object near the key frames. The proposed method extracts a dense boosting binary feature computed with a boosted binary hash function, and then pools the features in different resolutions. At last, the SVM with spatial pyramid kernel finishes the classification task. In this work, 8 bytes for the feature computed with a hash function that 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.