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基于内容的图像检索,用于传统的印尼织布图像,使用修改的卷积神经网络方法
Silvester Tena1,2, Rudy Hartanto1, Igi Ardiyanto1
1Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia.
Journal of imaging
|August 25, 2023
概括
本研究介绍了印度尼西亚伊卡特织布的TenunIkatNet数据集. 一个修改的卷积神经网络 (MCNN) 在图像检索中实现了高精度,帮助工匠和贸易.
科学领域:
- 计算机科学 计算机科学
- 织艺术 织艺术
- 文化遗产 文化遗产 文化遗产
背景情况:
- 基于内容的图像检索 (CBIR) 系统可以支持印尼传统织物工匠和贸易.
- 由于有限的数据集和需要同时考虑独特的织物特征,开发有效的CBIR系统具有挑战性.
研究的目的:
- 创建TenunIkatNet数据集,一个专门的印尼伊卡特织布图像集合.
- 开发和评估一个修改的卷积神经网络 (MCNN),以有效和准确地检索这些组织.
主要方法:
- 在120个类别的印尼伊卡特织物中收集了4800张图像.
- 在各种条件下捕获图像 (垂直,不同的背景,使用的形式).
- 采用修改的卷积神经网络 (MCNN) 来进行特征提取和图像检索.
主要成果:
- 该数据集包括120个类和4800个图像.
- 与已建立的预训练CNN模型相比,MCNN模型表现出卓越的性能.
- 实现了高的检索准确率: 99.96% (前五名), 99.88% (前十名), 99.50% (前20名) 和 97.60% (前50名).
结论:
- 开发的TenunIkatNet数据集和MCNN为印度尼西亚伊卡特织物检索提供了有效的解决方案.
- 这个系统可以显著地使工匠,文化保护和贸易促进工作受益.
- MCNN的表现突显了它在专门的图像检索任务中的潜力.
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