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基于DNA编码的核酸模式和深度特征用于实例和基于类的图像检索
IEEE transactions on nanobioscience
|August 11, 2023
概括
这项研究引入了DNA编码,用于高效的图像存储和检索,增强基于内容的图像检索 (CBIR) 系统. 新的DNA编码方法显著提高了跨不同数据集的检索准确性.
科学领域:
- 生物信息学是一种生物信息学.
- 计算机视觉 计算机视觉
- 数据存储数据存储数据存储
背景情况:
- 通过将信息表示为核酸序列 (A,C,T,G),DNA编码提供了高效的数据存储.
- 传统方法在存储效率和大图像数据集的检索准确性方面面临挑战.
研究的目的:
- 通过利用DNA编码原理来提高基于内容的图像检索 (CBIR) 的准确性.
- 开发一种新的DNA编码方案,用于紧和突出的图像信息表示.
主要方法:
- 实施了最重要的基于位平面的DNA编码方案,以从图像中生成DNA平面.
- 利用编码的DNA平面用于基于核酸模式的特征提取和图像检索.
- 应用深度学习架构 (ResNet-50,VGG-16/19,Inception V3) 用于使用翻译和放大DNA平面进行基于分类的检索.
主要成果:
- 拟议的DNA编码方案有效地以紧的格式捕获突出的视觉信息.
- 基于核酸模式和基于深度学习的检索表明性能有所改善.
- 在各种数据集 (珊瑚,物体,医疗图像) 上进行评估,其中包括134个类别的28,200张图像.
结论:
- 与现有的最先进的方法相比,DNA编码方法显著提高了图像检索准确度.
- 这种方法为CBIR系统中高效的图像存储和检索提供了一个有希望的方向.
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