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Visual Health Analysis of Print Advertising Graphic Design Based on Image Segmentation and Few-Shot Learning

机译:基于图像分割和小样本学习的平面广告平面设计视觉健康分析

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摘要

Graphics innovation must adapt to the changing trends of the times in order to stay ahead of the curve. This article investigates the use of graphic vision in print advertising design, using image segmentation as a starting point and examines the current state of visual innovation in print advertising graphic design and its various expressions and applications. A series of homogeneous regions are generated and outlined using an image segmentation algorithm based on adaptive local threshold. The user then paints different colors on the area sets that make up the various targets. Finally, the image segmentation is completed by merging the color mark region sets. Automatic extraction of the initial curve of an active contour model, construction of an active contour model based on saliency and level set solution, automatic selection of training samples when a classifier is used for image segmentation, and so on are all problems that this method effectively solves. Experiments show that this algorithm not only satisfies users’ demands for more intuitive input and more accurate interactive image segmentation results but also enables multiregion and multitarget image segmentation with ease.
机译:图形创新必须适应时代变化的趋势,才能保持领先地位。本文以图像分割为切入点,研究了平面广告视觉在平面广告设计中的应用,并探讨了平面广告平面设计中视觉创新的现状及其各种表现形式和应用。使用基于自适应局部阈值的图像分割算法生成并勾勒出一系列均匀区域。然后,用户在构成各种目标的区域集上绘制不同的颜色。最后,通过合并颜色标记区域集来完成图像分割。自动提取主动轮廓模型的初始曲线,基于显著性和水平集解构建主动轮廓模型,使用分类器进行图像分割时自动选择训练样本等都是该方法有效解决的问题。实验表明,该算法不仅满足了用户对更直观的输入和更准确的交互式图像分割结果的需求,而且能够轻松实现多区域、多目标的图像分割。

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