Colors in multimodal data: Dominant line extraction inspired by computer vision techniques

机译：多峰数据中的颜色：受计算机视觉技术启发的优势线提取

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摘要

In this paper, we propose a multiple line extraction method from multimodal data points in high dimensional space. It can sparsely represent multimodal sensor network data by utilizing high correlation among channels in the data. We exploit the idea of Color Lines, which is a model using high correlation among RGB channels in computer vision. It represents real color images as a collection of multiple lines in RGB color space. By extracting color lines from multimodal data, our proposed method can utilize hidden inter-channel relationships unlike conventional methods. We apply the proposed method for compressing a multimodal data matrix and show its effectiveness.

机译：在本文中，我们提出了从高维空间中的多峰数据点中提取多行的方法。通过利用数据中通道之间的高度相关性，它可以稀疏地表示多模式传感器网络数据。我们利用色线的概念，该模型是在计算机视觉中使用RGB通道之间的高度相关性的模型。它以RGB颜色空间中多行的集合形式表示实色图像。通过从多峰数据中提取色线，我们提出的方法可以利用隐藏的通道间关系，这与常规方法不同。我们将所提出的方法用于压缩多峰数据矩阵并显示其有效性。

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《International Geoscience and Remote Sensing Symposium》|2017年|9-12|共4页
会议地点 Fort Worth(US)
作者
Tomohiro Nishikawa; Yuichi Tanaka;
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Tokyo University of Agriculture and Technology Tokyo Japan;

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关键词
Principal component analysis; Image color analysis; Data mining; Correlation; Clustering algorithms; Estimation; Ocean temperature;

机译：主成分分析；图像色彩分析；数据挖掘;相关性聚类算法；估计；海洋温度;

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4. Colors in multimodal data: Dominant line extraction inspired by computer vision techniques [C] . Tomohiro Nishikawa, Yuichi Tanaka IEEE International Geoscience and Remote Sensing Symposium . 2017

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Colors in multimodal data: Dominant line extraction inspired by computer vision techniques

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