在CBIR中进行无监督的内容挖掘:利用潜在扩散来解释复杂的基于文本的查询
Venkata Rama Muni Kumar Gopu1, Madhavi Dunna1
1Department of EECE, GITAM School of Technology, GITAM Deemed to be University, Rushikonda, Visakhapatnam 530045, India.
Journal of imaging
|June 26, 2024
概括
本研究介绍了一种基于内容的图像检索 (CBIR) 的新方法,使用隐性扩散模型来解释复杂的文本提示. 这种方法有效地将文本描述与视觉内容连接起来,用于高级图像搜索.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 传统的基于内容的图像检索 (CBIR) 通常依赖于域特定的查询.
- 解释复杂的文本提示以获取图像是一个重大挑战.
研究的目的:
- 开发一种基于内容的图像检索 (CBIR) 的新方法,利用复杂的基于文本的查询.
- 利用潜在扩散模型来解释复杂的文本提示和生成视觉表示.
主要方法:
- 采用潜伏扩散模型来解释复杂的文本提示.
- 为特征表示和相似性评估设计了一个定制的三重网络.
- 利用等号相似性来获取基于特征表示的相关图像.
主要成果:
- 潜在扩散模型成功地将复杂的文本查询转化为视觉内容.
- 提出的方法有效地弥合了文本提示和没有标签或元数据的图像检索之间的差距.
- 展示了基于细微的文本描述来检索图像的能力.
结论:
- 新的CBIR方法为复杂的基于文本的图像检索提供了一种强大的方法.
- 隐性扩散模型和三重网络为将文本含义与视觉数据连接提供了强大的框架.
- 这一进步为未来的研究铺平了道路,用于用于大数据应用的生成AI驱动的图像检索.
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