Generalized N-Dimensional Independent Component Analysis Based Multiple Feature Selection and Fusion (医用画像)

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概要

• 論文の詳細を見る

• We proposed a multilinear independent component analysis framework called generalized N-dimensional ICA (GND-ICA) by extending the conventional linear ICA based on multilinear algebra. Unlike the linear ICA that only treats one-dimensional data, the proposed GND-ICA treats N-dimensional data as a tensor without any preprocess of data vectorization. We furthermore introduce two types of GND-ICA solutions and analysis their efficiency and effectiveness. As an application, the GND-ICA can be used for multiple feature fusion and representation for color image classification. Many features extracted from a given image are constructed as a tensor. The feature tensor can be effective represented by GND-ICA. Compared with conventional linear subspace learning methods, GND-ICA is capable of obtaining more distinctive representation for color image classification.

• 一般社団法人電子情報通信学会の論文
• 2012-05-10

著者

• Ai Danni

  Graduate School of Science and Engineering, Ritsumeikan University

• Ai Danni

  Graduate School Of Science And Engineering Ritsumeikan University

• Han Xianhua

  Graduate School Of Science And Engineering Ritsumeikan University

• Han Xian‐hua

  Ritsumeikan Univ. Kusatsu‐shi Jpn

• Duan Guifang

  Graduate School Of Engeneering And Science Ritsumeikan University

• Chen Yen-wei

  Graduate School Of Engineering And Science Ritsumeikan University

• Duan Guifang

  State Key Laboratory of CAD & CG, Zhejiang University:Graduate School of Science and Engineering, Ritsumeikan University

• Han Xianhua

  College of Information Science and Engineering, Ritsumeikan University

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