Data Transformation for Primitive Feature Extraction in Image Information Mining: a Comparative Study
نویسندگان
چکیده
Image transformation is the initial step for color texture image segmentation. Various techniques are available for the transformation along the spatial and spectral axes. This paper compares three methods of image transformation along the spectral axis HSV, PCA, and ICA. Quantitative comparison is performed in terms of the number of cluster estimated with different validity indexes – average Silhouette coefficients, index-I, and J-value. Experimental results show that the ICAspectral transformation provides reliable image segmentation when compared to other approaches. When the spectral transformation is performed after a 2D-DWT spatial transformation, marginal quantitative gain is obtained in coarse segmentation.
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