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Generally fuzzy c-means algorithm is one proved that very well suited for remote sensing image segmentation, exhibited sensitivity to the initial guess with regard to both speed and stability. But it also showed sensitivity to noise. This paper proposes a fully automatic technique to obtain image clusters. A modified fuzzy c-means classification algorithm is used to provide a fuzzy partition. This method is less sensitive to noise as it filters the image while clustering it, which is bgsed on the consideration of the neighbors as factors the attract pixels into their cluster. The experimental results on JERS-I Synthetic Aperture Radar (SAR) image demonstrate its potential usefulness.

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This paper proposes Two-Dimensional Linear Discriminant Analysis (2DLDA) for feature extraction which used for face recognition application. This method is developed from Fisher Linear Discrimnant (FLD) and Two-Dimensional Principle Component Analysis (2DPCA). In this method, 2DLDA directly uses the image matrix to calculate the between-class scatter matrix and within-class scatter matrix. Moreover, 2DLDA will be handling the problem that the within-class scatter matrix maybe singular. The experimental results indicated that the 2DLDA method is more computationally efficient than conventional methods.

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