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首页> 外文期刊>Arabian Journal for Science and Engineering >Homomorphic Normalization-Based Descriptors for Texture Classification
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Homomorphic Normalization-Based Descriptors for Texture Classification

机译：基于同态归一化的纹理分类描述符

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摘要

Illumination variation is an essential trait in texture analysis, since the same texture can be surrounded with different illuminations, which can greatly affect the classification rate. This paper introduces a new approach to extract the texture features applying illumination normalization-based texture descriptors. The prime objective is to enhance the classification accuracy by normalizing the illumination of colour textures. Normalization of illumination is achieved by applying homomorphic filter. For feature extraction, two relevant approaches grey-level co-occurrence matrix (GLCM) and Laws’ mask are utilized. Experiments are conducted for normalized co-occurrence and Laws’ filter for colour images. Classification rates of the traditional GLCM and Laws’ mask descriptors are included for baseline comparison. The effectiveness of the introduced techniques is assessed on three benchmark texture datasets, i.e. STex, VisTex, and ALOT. A k -nearest neighbour ( k -NN) classifier is utilized to perform texture classification. Results show that the proposed approach has achieved higher classification rates and outperformed existing methods.
机译：光照变化是纹理分析的一个基本特征，因为相同的纹理可以被不同的光照包围，这会极大地影响分类率。本文介绍了一种使用基于照明归一化的纹理描述符提取纹理特征的新方法。主要目的是通过归一化颜色纹理的照明来提高分类精度。照明的归一化是通过应用同态滤镜实现的。对于特征提取，使用了两种相关的方法：灰度共现矩阵（GLCM）和Laws遮罩。针对规范化的同现进行了实验，对彩色图像进行了Laws过滤。包括传统GLCM和Laws的蒙版描述符的分类率，用于基线比较。在三个基准纹理数据集（即STex，VisTex和ALOT）上评估了引入技术的有效性。利用k最近邻居（k -NN）分类器来执行纹理分类。结果表明，该方法具有较高的分类率，并且优于现有方法。

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