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

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 correl...

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Bibliographic Details
Published in:IEEE International Geoscience and Remote Sensing Symposium proceedings pp. 9 - 12
Main Authors: Nishikawa, Tomohiro, Tanaka, Yuichi
Format: Conference Proceeding
Language:English
Published: IEEE 01.07.2017
Subjects:
Clustering algorithms
color lines
compression
Correlation
Data mining
Estimation
Image color analysis
Multimodal data
Ocean temperature
Principal component analysis
sensor network
sparse coding
ISSN:2153-7003
Online Access:Get full text
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Description
Summary: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.
ISSN:2153-7003
DOI:10.1109/IGARSS.2017.8126820