Coniferous Trees NeedlesBased Taxonomy Classification This paper introduces multispectral rotationally invariant textural features of the Markovian type applied for the effective coniferous tree needles categorization. Presented texture features are inferred from the descriptive multispectral spiral widesense Markov model. Unlike the alternative texture recognition methods based on various grayscale discriminative textural descriptions, we take advantage of the needles texture representation, which is fully descriptive multispectral and rotationally invariant.The presented method achieves high accuracy for needles recognition. Thus it can be used for reliable coniferous tree taxon classification. Our classifier is tested on the open source needles database Aff, which contains 716 highresolution images from 11 diverse coniferous tree species.

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.