走向无代码书的深度概率量子化,用于图像检索
IEEE transactions on pattern analysis and machine intelligence
|October 13, 2023
概括
DeepIndex使用深度神经网络进行语义意识的图像检索,改进了传统的量化方法. 这种方法通过学习更具歧视性的特征空间分区来优化检索准确性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 量子化是可扩展的图像检索的关键技术,通常使用反向索引.
- 传统的Voronoi基于细胞的量子化与用于语义检索的区分空间分区作斗争.
研究的目的:
- 开发一种新的深度概率定量化方法,用于增强语义图像检索.
- 使用深度神经网络探索语义意识的特征空间分区.
主要方法:
- 提出了DeepIndex方法,一种深度概率定量化方法.
- 利用深度神经网络输出将图像分配到反向索引列表的概率.
- 通过在训练期间最大限度地提高检索准确性奖励来优化神经网络.
主要成果:
- 与现有的量子化方法相比,实现了更有语义区分的空间分区.
- 证明了DeepIndex在公共图像数据集上的有效性,用于语义图像检索.
结论:
- DeepIndex为语义图像检索提供了一种优越的替代传统量子化方法.
- 深度学习可以实现更有效的功能空间分区,以提高检索准确度.
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