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Mutual information (MI) has obvious potential for feature selection, but this has not been fully exploited in the past. In order to make numerical computation easier and more accurate, the MI of the whole multi-dimensional data can be decomposed into an amount of one-dimensional MI and one-dimensional conditional MI components. This paper reveals that using one-dimensional MI components to replace...
Many feature selection methods have been proposed in recent years, but there is little work concerning the evaluation of the performances with respect to different feature selection methods especially when the ground truth map is unavailable. In this paper, a new method called quantitative multivariate correlation analysis (QMCA) is proposed, which provides a quantitative measure of the useful information...
Performance evaluation method that can compare and analyze different fusion techniques is an essential part in the development of image fusion techniques. This paper mainly discusses the applications and restrictions of performance evaluation metrics such as statistics feature related, information theory related, correlation information entropy, information deviation and etc. Accurate and effective...
Image fusion is becoming one of the hottest techniques in image processing. The paper proposes a shared chain mutual information concept to quantitatively and objectively evaluate the performances of difference image fusion techniques. Furthermore, the performances of two widely used image fusion techniques, i.e. wavelet transform based fusion and pyramid transform based fusion operating on a typical...
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