Colors in multimodal data: Dominant line extraction inspired by computer vision techniques
Colors in multimodal data: Dominant line extraction inspired by computer vision techniques
复制标题
多模态数据中的颜色：受计算机视觉技术启发的主线提取
DOI：
10.1109/igarss.2017.8126820
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Y. Tanaka
中科院分区：
文献类型：
--
作者：
T. Nishikawa;Y. Tanaka
本文提出了一种高维空间中多模态数据点的多线提取方法。利用数据通道间的高度相关性，可以稀疏地表示多模态传感器网络数据。我们利用颜色线的思想，这是一种利用计算机视觉中RGB通道之间的高相关性的模型。它将真实彩色图像表示为RGB色彩空间中多条线的集合。通过从多模态数据中提取颜色线，与传统方法不同，我们的方法可以利用隐藏的通道间关系。我们将该方法应用于多模态数据矩阵的压缩，并证明了它的有效性。
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.