A Quantitative and Comparative Analysis on Variations of a Method for Segmentation of Similar Images Segmenting images is one of the most famous image processing and computer visionu0027s challenge. In this work we present an approach to a method of segmentation of similar images based on hierarchical segmentation and texture information. An empirical study was conducted, it was made of three experiments. First experiment evaluated the  P  parameteru0027s variation of the LBP texture extractor and the use of external edges of analyzed region; the second one evaluated the variation of eight gradients, being 7 colored gradients; and the last one compares results between KullbackLeibler measure and Euclidean distance. The obtained results show the best configurations.

A Rotation and Scale Invariant Texture Description Approach This paper presents a novel texture description approach, which is robust to variances in rotation, scale and illumination in images, to classify the texture of images. A limitation with traditional methods is that they are more or less sensitive to the mentioned changes in images. To overcome this problem, we propose a novel Local Haar Binary Pattern (LHBP) based framework to ensure invariance in global rotation, scale, and light change. Our method consists of two components: feature extraction and scale selfadaptive classification. The global rotation invariant LHBP histogram features are extracted against the variances of illumination and global rotation, and the scale selfadaptive strategy is used for optimizing the classification of different scale textures. Evaluation results on Outex and Brodatz databases illustrate the significant advantages of the proposed approach over existing algorithms.