Image Processing Technology Based on Machine Learning

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Abstract
Machine learning is a relatively new field. With the deepening of people's research in this field, the application of machine learning is increasingly extensive. On the other hand, with the advancement of science and technology, graphics have been an indispensable medium of information transmission, and image processing technology is also booming. However, the traditional image processing technology, more or less has some defects. This article introduces machine learning into image processing, and studies the image processing technology based on machine learning. This article summarizes the current popular image processing technology, compares various image technologies in detail, and explains the limitations of each image processing method. In addition, on the basis of image processing, this article introduces machine learning algorithm, applies convolution neural network to feature extraction of image processing, and carries out simulation test. In the test, we select VOC2007 dataset for image segmentation, ImageNet dataset for target detection, CIFAR100 dataset for image classification, and ROC curve for performance evaluation. The results show that the algorithm based on deep learning can achieve high accuracy in image segmentation, classification, and target detection. The accuracy of image segmentation is 0.984, the accuracy of image classification is 0.987, and the accuracy of target detection is 0.986. Thus, image processing based on machine learning has great advantages.

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