This paper presents a novel approach combining convolutional layers (CLs) and large-margin metric learning for training supervised models on small datasets texture classification. The core of such an is loss function that computes the distances between instances interest support vectors. objective to update weights CLs iteratively learn representation with large margin classes. Each iteration r...

Texture analysis plays an increasingly important role in computer vision. Since the textural properties of images appear to carry useful information for discrimination purposes, it is important to develop significant features for texture. This paper presents a novel technique for texture extraction and classification. The proposed feature extraction technique uses 2D–DFT transformation. A combi...

This paper uses theory, simulation, and laboratory experiment, to show that directional illumination, used during the image acquisition process, acts as a directional filter of three dimensional texture. It is shown that the directional characteristics of image texture are not intrinsic to the physical texture being imaged, as they are affected by the direction of the illumination. This result ...

We discuss features extracted from a wavelet packet decomposition for image classification. Statistical features computed from wavelet packet coefficients are compared to structural features which are derived from an image dependent wavelet packet decomposition subband structure. Primary application area is the classification of pit pattern structures in zoom-endoscopic colon imagery, while res...

Introduction: Differentiation of benign and malignant liver tumors is very important for finding appropriate treatment procedure. Human eyes sometime are not able to diagnose the type of liver tumor. Texture analysis is considered as a suitable method to increase the diagnostic power of medical images. In this study texture analysis is employed in order to classification of ben...