基于内容的印度传统织品图案的图像检索,使用深度功能融合
Seema Varshney1, Sarika Singh2, C Vasantha Lakshmi2
1Department of Physics and Computer Science, Dayalbagh Educational Institute, Agra, 252008, India. seema@dei.ac.in.
Scientific reports
|May 1, 2024
概括
这项研究介绍了用于时装设计的AI系统,使用深度学习来检索传统的印度织图案. 它通过将独特的图案与当前趋势相匹配来加速设计周期,帮助设计师.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 织品设计的设计
背景情况:
- 时装行业面临着将传统的织设计与现代设计趋势相结合的挑战.
- 计算机化威胁着手工艺术技能,需要高效的设计工具.
- 在线购物的增长增加了对独特和文化丰富的织品设计的需求.
研究的目的:
- 为传统印度织品开发一个基于内容的交互式图像检索 (CBIR) 系统.
- 通过将人类的创造力与人工智能能力相结合,协助时装设计师.
- 通过高效的图案检索和趋势关联,加速设计周期.
主要方法:
- 利用InceptionV3和InceptionResNetV2模型中的深度功能进行图像匹配.
- 实现基于相似性的细粒度突出度图 (SBFGSM) 方法用于特征可视化.
- 结合深度特征融合与PCA维度缩小和聚类以实现更快的搜索.
- 使用相关性反来完善图像检索结果.
主要成果:
- 拟议的CBIR系统在Caltech-101,Corel-1K和印度织品数据集上的现有方法相比表现出更好的表现.
- 该系统有效地将查询设计与传统印度织品数据库进行匹配.
- 使用SBFGSM的特征可视化突出了重要的设计元素.
结论:
- 开发的基于人工智能的CBIR系统增强了传统印度织图案的检索.
- 这种工具可以通过加快设计过程,使时装设计师受益显著.
- 它提供了一种可行的解决方案,通过技术整合来保护织设计中的文化遗产.
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