Textured Images Classification Using Nearest Feature Line Method Added with Kurtosis and Skewness

In this study, we try to compare the performances of various textured images classification methods, all based on multi resolution decompositions. The images tested are extracted from the Brodatz album. For decomposition, we use the Separable (SWT) and NonSeparable Wavelet Transforms (NSWT). The sub images obtained allow extracting feature vectors. Two types of classes are made up and Nearest Feature Line Method is used to classify each sub image. The performances are evaluated by calculating the classification error rates and the retrieval efficiency in each case. Lastly, we try to identify the factors influencing the performances.