论文标题
用于基于内容的图像搜索的新的本地ra描述符
A new Local Radon Descriptor for Content-Based Image Search
论文作者
论文摘要
基于内容的图像检索（CBIR）是计算机视觉研究的重要组成部分，尤其是在医学专家系统中。在CBIR系统中，希望具有最少的调整参数的判别图像描述符。在本文中，我们基于局部ra的直方图引入了一个新的简单描述符。我们还提出了一个非常快速的基于卷积的局部ra估计器，以克服ra径的缓慢过程。我们使用病理图像（kimiapath24）和肺CT斑块进行了实验，并测试了我们提出的医疗图像处理解决方案。与其他基于直方图的描述符（例如LBP和HOG）以及一些预先训练的CNN相比，我们取得了卓越的结果。
Content-based image retrieval (CBIR) is an essential part of computer vision research, especially in medical expert systems. Having a discriminative image descriptor with the least number of parameters for tuning is desirable in CBIR systems. In this paper, we introduce a new simple descriptor based on the histogram of local Radon projections. We also propose a very fast convolution-based local Radon estimator to overcome the slow process of Radon projections. We performed our experiments using pathology images (KimiaPath24) and lung CT patches and test our proposed solution for medical image processing. We achieved superior results compared with other histogram-based descriptors such as LBP and HoG as well as some pre-trained CNNs.
