| Summary: | Curve feature description is an important issue in the field of image matching. In the past years, this problem has been studied mainly based on handcrafted methods. To conquer the disadvantages of low discrimination and weak robustness of curve feature description under complex conditions, a Mean-Standard Deviation Curve Descriptor based on Deep learning (D-MSCD) is proposed in this paper. Firstly, a large-scale curve feature dataset with 210,000 labeled curve image patches is constructed for training and testing. After longitudinally compressing the support areas of the curve in each image into the support areas of points, the mean and standard deviation image patches of each curve are obtained, then the curve image patch is uniquely represented. Secondly, a modified L2-Net(DSM) which is a network architecture with dilated convolution is constructed to improve the performance of curve descriptors, and the experimental results on the Brown dataset show the mean FPR95 value is reduced by 17.48%. Finally, the modified L2-Net(DSM) is trained on the large-scale curve feature dataset and the model of D-MSCD is obtained, it achieves the best matching performance in every image change, and the average matching performance on the Oxford dataset is improved by 13.09%. Experimental results demonstrate the proposed D-MSCD has better effectiveness than the traditional handcrafted curve descriptors.
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