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首页> 外文期刊>Journal of Imaging Science and Technology >Kernel density estimators for hue based image retrieval
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Kernel density estimators for hue based image retrieval

机译：基于色调的图像检索的核密度估计器

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摘要

Color is widely used for content-based image retrieval. In these applications the color properties of an image are characterized by the probability distribution of the colors in the image. These probability distributions are very often estimated by histograms although the histograms have many drawbacks compared to other estimators such as kernel density methods. In this article we investigate whether using kernel density estimators instead of histograms could give better retrieval results based on hue descriptors of color images. In this article we introduce the Fourier series coefficients as descriptors of hue distributions. We argue that under certain conditions these coefficients are optimal in a least squared error sense. We will also apply Parseval formula to compute the similarity of two distributions directly from these Fourier coefficients. Our experiments show that this modification of the kernel based similarity estimation has better retrieval performance than the histogram methods and we will also show that the method is insensitive to parameter changes as long as they are selected in a reasonable range.
机译：颜色被广泛用于基于内容的图像检索。在这些应用中，图像的颜色特性通过图像中颜色的概率分布来表征。尽管与其他估计量（例如核密度方法）相比，直方图有很多缺点，但通常会通过直方图来估计这些概率分布。在本文中，我们研究了使用核密度估计值而不是直方图是否可以基于彩色图像的色相描述符提供更好的检索结果。在本文中，我们介绍了傅里叶级数系数​​作为色相分布的描述子。我们认为，在某些条件下，这些系数在最小二乘误差意义上是最优的。我们还将应用Parseval公式直接从这些傅立叶系数计算两个分布的相似度。我们的实验表明，对基于核的相似度估计进行的这种修改比直方图方法具有更好的检索性能，并且我们还将证明，只要在合理的范围内进行选择，该方法对参数更改不敏感。

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