Evaluation of the Texture Analysis Using Spectral Correlation Function
In this paper a new feature extraction technique for texture analysis is evaluated. This method is based on spectral correlation function (SCF) that provides a second-order statistical description in the frequency domain of signals. Two one dimensional signals are obtained from each image by ordering of pixels row by row and column by column. Then the SCF of each signal is calculated by a computational efficient algorithm, namely, the FFT accumulation method (FAM). Features are energy and standard deviation of spectral correlation functions at different regions of bifrequency planes. This scheme shows high performance in the retrieval and classification of Brodatz texture images. Experimental results indicate that the proposed method improves retrieval accuracy and correct classification rate in comparing with other approaches. Furthermore the evaluation of the hidden layer output at the classifier with different numbers of neurons indicates that the extracted features from SCF are more separable potentially in comparing with traditional discrete wavelet transform approaches.
texture analysis spectral correlation function feature extraction texture classification texture retrieval