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JBE, vol. 24, no. 2, pp.234-242, March, 2019


SIFT Image Feature Extraction based on Deep Learning

Jae-Eun Lee, Won-Jun Moon, Young-Ho Seo, and Dong-Wook Kim

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In this paper, we propose a deep neural network which extracts SIFT feature points by determining whether the center pixel of a cropped image is a SIFT feature point. The data set of this network consists of a DIV2K dataset cut into 33×33 size and uses RGB image unlike SIFT which uses black and white image. The ground truth consists of the RobHess SIFT features extracted by setting the octave (scale) to 0, the sigma to 1.6, and the intervals to 3. Based on the VGG-16, we construct an increasingly deep network of 13 to 23 and 33 convolution layers, and experiment with changing the method of increasing the image scale. The result of using the sigmoid function as the activation function of the output layer is compared with the result using the softmax function. Experimental results show that the proposed network not only has more than 99% extraction accuracy but also has high extraction repeatability for distorted images.     

Keyword: SIFT Feature extraction, Deep learning, VGG, CNN(Convolutional Neural Network), Repeatability

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