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Padua point interpolation and Lp-norm minimisation in colour-based image indexing and retrieval

机译：基于彩色的图像索引和检索中的帕多瓦点内插和Lp范数最小化

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摘要

Colour has proven to be a very powerful feature for image indexing. Many examples of image retrieval systems based on colour or chromaticity histograms have been proposed, following on from the histogram intersection method of Swain and Ballard. Here the authors introduce a compact representation of the chromaticity histogram which is based on the Padua point interpolation technique. Specifically, the histogram is represented as a linear combination of Chebyshev polynomials. This bounds a certain maximum deviation, as opposed to a least-squares criterion used in previous work. With this in mind, the minimisation of different Lp norms and the L;1; norm of the error is compared. After presenting the Padua point image indexing and retrieval method, the authors compare its performance to the histogram intersection, the discrete cosine transform, and a dataset oriented method based on principal component analysis. The experiments show that the Padua points match and, in some cases, improve the performance of these methods. This is significant as the proposed method is not tuned (unlike the PCA approach to any dataset). Finally, the behaviour of the Padua point method is analysed in relation to the minimisation of different norms.
机译：事实证明，颜色是图像索引的一项非常强大的功能。继斯温和巴拉德的直方图相交方法之后，已经提出了许多基于颜色或色度直方图的图像检索系统的示例。在这里，作者介绍了基于帕多瓦点插值技术的色度直方图的紧凑表示。具体而言，直方图表示为Chebyshev多项式的线性组合。与先前工作中使用的最小二乘标准相反，这限制了一定的最大偏差。考虑到这一点，最小化了不同的Lp规范和L; 1;比较错误的范数。在提出帕多瓦点图像索引和检索方法之后，作者将其性能与直方图交点，离散余弦变换和基于主成分分析的面向数据集的方法进行了比较。实验表明，Padua点匹配，并且在某些情况下可以提高这些方法的性能。这很重要，因为未对建议的方法进行调整（与对任何数据集的PCA方法不同）。最后，分析了帕多瓦点法与不同规范的最小化有关的行为。

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