论文标题
部分可观测时空混沌系统的无模型预测
Texture image analysis based on joint of multi directions GLCM and local ternary patterns
论文作者
论文摘要
人类视觉大脑使用三个主要成分，例如颜色，纹理和形状来检测或识别环境和对象。因此，在过去的二十年中，科学研究人员对纹理分析引起了很多关注。纹理功能可以在通勤视觉或机器学习问题中的许多不同应用中使用。从现在开始，已经提出了许多不同的方法来对纹理进行分类。他们中的大多数将分类准确性视为应改进的主要挑战。在本文中，提出了一种新方法，基于两个有效纹理描述符，共发生矩阵和局部三元模式（LTP）的组合。首先，进行基本的本地二进制模式和LTP以提取本地纹理信息。接下来，从灰度共发生矩阵中提取统计特征的子集。最后，串联功能用于训练分类器。根据准确性，在Brodatz基准数据集上评估了该性能。实验结果表明，与某些最新方法相比，提出的方法提供了更高的分类率。
Human visual brain use three main component such as color, texture and shape to detect or identify environment and objects. Hence, texture analysis has been paid much attention by scientific researchers in last two decades. Texture features can be used in many different applications in commuter vision or machine learning problems. Since now, many different approaches have been proposed to classify textures. Most of them consider the classification accuracy as the main challenge that should be improved. In this article, a new approach is proposed based on combination of two efficient texture descriptor, co-occurrence matrix and local ternary patterns (LTP). First of all, basic local binary pattern and LTP are performed to extract local textural information. Next, a subset of statistical features is extracted from gray-level co-occurrence matrixes. Finally, concatenated features are used to train classifiers. The performance is evaluated on Brodatz benchmark dataset in terms of accuracy. Experimental results show that proposed approach provide higher classification rate in comparison with some state-of-the-art approaches.
