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Improving image retrieval using combined features of Hough transform and Zernike moments

机译：利用Hough变换和Zernike矩的组合功能改善图像检索

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摘要

In this paper, a novel solution to content based image retrieval system is provided by considering both local and global features of the images. Local features extraction is done by computing histograms of distances from edge lines to the centroid of edge image, where edge lines are detected using Hough transform. It is a robust and effective method to provide association among adjacent edge points, which represent their linear relationship with each other. Zernike moments are used to describe the global features. We have applied algorithms for the fast computation of Hough transform and Zernike moments to make our system fast and efficient. Bray-Curtis similarity measure is applied to compute the similarity among images. A large number of experiments are carried out to evaluate the system performance over six standard databases, which represent various kinds of images. The results reveal that the proposed descriptors and the Bray-Curtis distance measure outperform the existing methods of image retrieval.
机译：本文通过考虑图像的局部和全局特征，为基于内容的图像检索系统提供了一种新颖的解决方案。局部特征提取是通过计算从边缘线到边缘图像的质心的距离的直方图完成的，其中使用霍夫变换检测边缘线。提供相邻边缘点之间的关联（表示彼此之间的线性关系）是一种鲁棒而有效的方法。 Zernike矩用于描述全局特征。我们已将算法用于霍夫变换和Zernike矩的快速计算，以使我们的系统快速高效。使用Bray-Curtis相似度度量来计算图像之间的相似度。在代表各种图像的六个标准数据库上进行了大量实验以评估系统性能。结果表明，提出的描述符和Bray-Curtis距离度量优于现有的图像检索方法。

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