| 以下仅列出文章摘要、目录等部分内容，如需获取完整论文资料，或原创定制、长期合作，请随时联系。 微信QQ:312050216 |
 |
 |
| 基于图像美学质量评估的图像自动裁剪 摘 要 作为图像美学质量最为主要的影响因素，构图主要是通过图像的裁剪进行后期调整和优化的，图像构图的优化为图像整体美学质量的提升提供了强有力的支持。所以，当前计算机视觉领域中， 如何通过图像自动化裁剪以全面提升图像的美学质量，始终是研究的重点方向，并且得到了广泛的关注和重视。在实际应用中，自动裁剪能够去除图像中的冗余或不美观的区域，是一种提高图像质量的常见操作。在每天都有大量的图像数据上传到互联网上的大数据时代，图像裁剪是图像处理的高频需求。人工评价图像的审美质量和人工裁剪图像是一项费时费力的工作。优质的基于美学质量评价的图像自动裁剪算法，可通过专业计算提高图像的美感，以应用于图像编辑和摄影辅助校正。能帮助人们节省大量思考构图的时间，提高了工作效率，增加了图像裁剪的专业性和便利性。而传统图像裁剪方法大都具有裁剪窗口纵横比固定，产生大量裁剪窗口导致系统运行效率极其低下的问题。且裁剪效果常常不尽人意。基于强化学习的图像裁剪算法将审美图像裁剪公式化为顺序决策过程，与以前的弱监督方法相比，此算法以更少的候选窗口和更少的时间实现了更先进的性能，提高了裁剪后的构图质量，并解决了滑动窗口机制窗纵横比固定的问题。 关键词：图像裁剪；图像美学质量评估；深度学习 Abstract As the most important factor affecting the aesthetic quality of images, composition is mainly adjusted and optimized in the later stage through image cutting, and the optimization of image composition provides strong support for the improvement of the overall aesthetic quality of images. Therefore, in the current field of computer vision, how to improve the aesthetic quality of images through automatic cropping has always been the focus of research, and has received extensive attention and attention. In practical applications, automatic cropping can remove redundant or unsightly areas in images, which is a common operation to improve image quality. In the era of big data, when a large amount of image data is uploaded to the Internet every day, image cropping is a high frequency requirement of image processing. It is a time-consuming and laborious task to manually evaluate the aesthetic quality of images and manually cut out images. High-quality image automatic cropping algorithm based on aesthetic quality evaluation can improve the aesthetic feeling of images through professional calculation, which can be applied to image editing and photography auxiliary correction. It can help people save a lot of time to think about composition, improve work efficiency, and increase the professionalism and convenience of image cutting. However, most of the traditional image cropping methods have the problem that the aspect ratio of cropping windows is fixed, and a large number of cropping windows are generated, which leads to the extremely low efficiency of the system. And the cutting effect is often unsatisfactory. Image clipping algorithm based on reinforcement learning formulated aesthetic image clipping as a sequential decision process. Compared with previous weak supervision methods, this algorithm achieved more advanced performance with fewer candidate windows and less time, improved the composition quality after clipping, and solved the problem of fixed aspect ratio of sliding window mechanism window. Key words: image clipping, image aesthetic quality evaluation, deep learning 目录 1 绪论4 1.1 研究背景和意义4 1.1.1 研究现状4 1.1.2 本文研究工作6 2 理论基础7 2.1 卷积神经网络7 3 基于图像美学质量评估的图像自动裁剪8 3.1图像美学质量评估8 3.2 图像自动裁剪方法8 3.2.1裁剪模型9 3.2.2网络框架9 4 实验分析11 4.1实验设置11 4.2 实验结果11 4.3 评估指标12 结论和展望12 参考文献13 |
