In this paper, we present a model that automatically differentiates copied versions of original images. Unlike traditional image copy detection schemes, our system is a Convolutional Neural Networks (CNN) based model which means that it does not need any manuallydesigned features. In addition, a convolutional network is more applicable to image copy detection whose architecture is designed for robustness to geometric distortions. Our model uses fully connected layers to compute a similarity between CNN features, which are extracted from image pairs by a deep convolutional network. This method is very efficient and scalable to large databases. In order to see the comparison visually, a variety of models are explored. Experimental results demonstrate that our model presents surprising performance on various data sets.
CITATION STYLE
Zhang, J., Zhu, W., Li, B., Hu, W., & Yang, J. (2016). Image copy detection based on convolutional neural networks. In Communications in Computer and Information Science (Vol. 663, pp. 111–121). Springer Verlag. https://doi.org/10.1007/978-981-10-3005-5_10
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