Abstract
This paper presents a simple and effective method to verify similar vehicle images. In order to provide a meaningful interpretation of the verification, we propose to detect the local differences between two images. We frame this task as a saliency map regression problem, where the saliency map measures the degree of discrepancy at every pixel. To achieve this goal, we use a convolutional neural network (CNN) to map two aligned vehicle images to one saliency map. Our network design enables end-to-end training. We validate our algorithm on a vehicle image dataset. Experimental results show that our approach is accurate, fast and robust, and it achieves better performance than other methods.
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Hong, L., Wang, W., Pang, Y., Hu, H., Tang, J. (2018). Interpretable Verification of Visually Similar Vehicle Images Using Convolutional Networks. In: Xu, Z., Gao, X., Miao, Q., Zhang, Y., Bu, J. (eds) Big Data. Big Data 2018. Communications in Computer and Information Science, vol 945. Springer, Singapore. https://doi.org/10.1007/978-981-13-2922-7_13
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DOI: https://doi.org/10.1007/978-981-13-2922-7_13
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