Academy of Fine Arts, Inner Mongolia Minzu University, Tongliao 028000, China.
College of Computer Science and Technology, Inner Mongolia Minzu University, Tongliao 028000, China.
Comput Intell Neurosci. 2022 Jun 20;2022:3242960. doi: 10.1155/2022/3242960. eCollection 2022.
Inner Mongolia is rich in grassland tourism resources, and the development of grassland tourism is of great significance to Inner Mongolia tourism and promotion of grassland protection. To better promote the grassland tourism of the Silk Road culture, the Conditional Global Area Network (CGAN) and Morphology Connected Component Chan-Vase (MCC-CV) algorithm are used to enhance and segment the traditional embroidery patterns in Inner Mongolia. Firstly, the generative adversarial network (GAN) is optimized, and a new GAN is proposed with the feature vector extracted from the convolutional neural network (CNN) as the constraint condition. Secondly, the automatic segmentation algorithm of embroidery based on the MCC-CV model is proposed, and finally, the proposed algorithm is tested. The test results demonstrate that after 8000 iterations of the proposed image-enhancement algorithm, its personalized features are enhanced, and the segmentation accuracy of the proposed image segmentation algorithm is 60%. The proposed algorithm provides some ideas for the application of deep learning (DL) technology in the grassland tourism of the Silk Road culture and also helps operators to accurately grasp the market and make tourists more comfortable and pleasant.
内蒙古拥有丰富的草原旅游资源，发展草原旅游对内蒙古旅游业和草原保护具有重要意义。为了更好地推广丝绸之路文化草原旅游，使用条件全局网络（CGAN）和形态连通分量 Chan-Vase（MCC-CV）算法来增强和分割内蒙古传统刺绣图案。首先，优化生成对抗网络（GAN），提出了一种新的 GAN，其特征向量是从卷积神经网络（CNN）中提取出来的。其次，提出了一种基于 MCC-CV 模型的刺绣自动分割算法，最后对所提出的算法进行了测试。测试结果表明，在经过 8000 次迭代的图像增强算法后，其个性化特征得到了增强，并且所提出的图像分割算法的分割精度为 60%。该算法为深度学习（DL）技术在丝绸之路文化草原旅游中的应用提供了一些思路，也有助于运营商准确把握市场，让游客更加舒适和愉快。
