自然图像的局部线性嵌入聚类算法
Locally Linear Embedding Clustering Algorithm for Natural Imagery
摘要：
描述自然图像颜色内容的能力是图像处理的一个重要应用。图像的逐像素着色可以自然地被视为颜色空间中的点，并且这些点的固有结构和分布通过聚类提供图像中的颜色信息的量化。在本文中，我们提出了一种新的拓扑驱动的聚类算法，允许分割的颜色特征的数字图像。该算法融合了局部线性嵌入（LLE）和矢量量化的颜色信息映射到一个较低的维空间，识别不同的颜色区域，并分类像素在一起的基础上的接近度测量和颜色内容。据观察，这些技术允许显着降低颜色分辨率，同时保持图像的视觉上重要的功能。
英文摘要：
The ability to characterize the color content of natural imagery is an important application of image processing. The pixel by pixel coloring of images may be viewed naturally as points in color space, and the inherent structure and distribution of these points affords a quantization, through clustering, of the color information in the image. In this paper, we present a novel topologically driven clustering algorithm that permits segmentation of the color features in a digital image. The algorithm blends Locally Linear Embedding (LLE) and vector quantization by mapping color information to a lower dimensional space, identifying distinct color regions, and classifying pixels together based on both a proximity measure and color content. It is observed that these techniques permit a significant reduction in color resolution while maintaining the visually important features of images.
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