Statistical multi-scale laws’ texture energy for texture segmentation
Mega Kusuma Wardhani, Xingru Yu, Jinping Li · 2018
Nowadays it is one of the main focuses to recognize objects in a digital image. The texture is an important valuable feature in describing the coarseness and the regularity pattern in the surface of an object. We present an interesting and effective technique for segmentation of different texture by integrating color information and Laws’ texture energy. The first step is to convert an image from RGB to HSV color space to obtain hue channel as the basic feature. The second step is to calculate Laws’ texture energy in each pixel by exploring statistical approaches including mean and variance in the serial of multi-scale windows by moving window, in this step several variances can be produced to form a vector, and the vector can be used as an additional feature. This work utilizes threshold of difference between neighborhood vectors as an alternative to distinguish coarseness in a region after segmentation by using the basic feature. In addition, this work calculates the difference mean of hue each color in a region which contain many colors in 5 × 5 window size and utilizes threshold of mean to distinguish the similarity mean between colors. This work examined images from Berkeley Segmentation Dataset (BSDS) which have several textures by using a threshold of difference (70) between neighborhood vectors and threshold mean (10) of hue. The results show that 70.6% of the texture segmentation can be accepted after combining color information and Laws’ texture energy and provide a favorable result for texture segmentation.