Retrieval of Non-Homogenous Textures Based on Directionality

Abstract

Most of the natural textures are non-homogenous and stochastic by nature. In many cases these textures are also directional. In this paper we present a method for the retrieval of the non-homogenous directional textures. This method is directional histogram and it represents the directional distribution of the texture. The histogram can be formed using either directional filtering method or Hough transform. We test the retrieval ability of our methods using rock texture images. The directional histogram method gives clearly better retrieval results than Gabor features and co-occurrence matrix.

Research on Green Consumption Based on Visual Evaluation Method—Evidence from Stone Flooring Industry

“…The images can be transformed to HSV color space, a small number of representative colors are quantified, and texture characteristics are retrieved based on the uniform pattern of rotated RLBP (rotated local binary pattern) [38]. Lepistö et al used a non-uniform texture image, partitioned into several regions of the texture features derived by the co-occurrence matrix, to distinguish the rock texture as a color parameter [39]. It has been noted that using different scales of the RGB and HIS color spaces, and Gaussian filtering of the color channels of images to obtain low-dimensional feature vectors, can improve the accuracy of stone classification.…”

Pattern defined heuristic rules and directional histogram based online ECG parameter extraction

“…2), where the plot has one real value at one moment of time, this method may not function properly. To obtain the baseline and QRS regions from this kind of ECG plots, an approach based on the concept of directional histogram, used in image processing for texture analysis approaches [29] or similarity measurement of 3D objects [30], is employed.…”

Rock image classification using color features in Gabor space

“…Classification of natural images is demanding, because in the nature the objects are seldom homogenous. For example, when the images of rock surface are inspected, there are often strong differences in directionality, 1 granularity, or color of the rock, even if the images represented the same rock type. These kinds of variations make it difficult to classify these images accurately.…”

“…The directionality and granularity of nonhomogenous natural textures have been discussed in our earlier work. 1,3 In addition to texture, color is also an essential feature of natural images. In this study, we combine the color information to the textural features of rock images.…”