Artificial Neural Network for Texture Classification Using Several Features: a Comparative Study

Texture analysis plays an essential and a major rule in image classification and segmentation in a wide range of applications such as medical imaging, remote sensing and industrial inspection. In this paper, we review the well known approaches of texture feature extraction and perform a comparative study between them. These approaches are namely gray level histogram, edge detection, and co-occurrence matrices, besides Gabor and Biorthogonal wavelet transformations. The feed forward artificial neural network (ANN) with backpropagation algorithm (BPA) is used as a supervised classifier. Experiments are conducted on two different datasets taken from multi-class engineering surfaces produced by six machining processes and from Brodatz (1966) textures album respectively. The classification accuracy is tested for both datasets, while the quality of estimation is tested for surface roughness parameters of the machined surfaces dataset only based on the roughness parameters evaluated from a contact measurement test.