Color texture image classification based on fractal features and extreme learning machine

Authors

ERKAN TANYILDIZI

DOI

10.3906/elk-1301-220

Abstract

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.

Keywords

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

First Page

2333

Last Page

2343

Recommended Citation

TANYILDIZI, ERKAN (2015) "Color texture image classification based on fractal features and extreme learning machine," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 23: No. 7, Article 24. https://doi.org/10.3906/elk-1301-220
Available at: https://journals.tubitak.gov.tr/elektrik/vol23/iss7/24