Wavelet feature extraction involves transforming images into wavelet coefficients. The number of data elements per image is determined based on the image’s rows and columns, typically brought to a power of two. The total number of values after applying the wavelet is calculated. Classification employs a Dual Statistical Approach, combining parametric and non-parametric methods like MEAN, NORM, Variance, and KS TEST. The Minimum Distance Classifier, utilizing Euclidean, Normalized Euclidean, and Mahalanobis distances, aids in classifying unknown image data by minimizing distances between data and classes in multi-feature space.
