Turkish Journal of Electrical Engineering and Computer Sciences




Texture classification, especially color texture classification, is considered a significant step in segmentation and object classification. The property of color and texture is important for characterizing objects in natural scenes. Fractal dimension (FD) has many applications in the field of image compression and image segmentation. A series of FD features, such as mean, standard deviation, lacunarity, kurtosis, skewness, entropy, inverse difference moment, contrast, energy, dissimilarity, homogeneity, and maximum probability, are investigated for obtaining the maximum discrimination. In this manuscript, a methodology is proposed that is based on FD and an extreme learning machine for color texture classification. Performance of the proposed methodology is evaluated by comprehensive experiments on a publicly available data set. The experiments show that the proposed methodology has advantages over other color texture analysis methods.


Color texture classification, differential box counting, fractal dimension, feature extraction, extreme learning machine

First Page


Last Page