Feature Extraction Technique using Discrete Wavelet Transform for Image Classification

K. Ghazali, M. Mansor, M. Mustafa, A. Hussain
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引用次数: 85

Abstract

The purpose of feature extraction technique in image processing is to represent the image in its compact and unique form of single values or matrix vector. Low level feature extraction involves automatic extraction of features from an image without doing any processing method. In this paper, we consider the use of high level feature extraction technique to investigate the characteristic of narrow and broad weed by implementing the 2 dimensional discrete wavelet transform (2D-DWT) as the processing method. Most transformation techniques produce coefficient values with the same size as the original image. Further processing of the coefficient values must be applied to extract the image feature vectors. In this paper, we propose an algorithm to implement feature extraction technique using the 2D-DWT and the extracted coefficients are used to represent the image for classification of narrow and broad weed. Results obtained suggest that the extracted 2D-DWT coefficients can uniquely represents the two different weed type.
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基于离散小波变换的图像分类特征提取技术
在图像处理中,特征提取技术的目的是将图像以单一值或矩阵向量的紧凑而独特的形式表示出来。低级特征提取是指在不做任何处理的情况下,从图像中自动提取特征。本文采用二维离散小波变换(2D-DWT)作为处理方法,考虑利用高级特征提取技术来研究窄杂草和宽杂草的特征。大多数变换技术产生的系数值与原始图像大小相同。为了提取图像特征向量,必须对系数值进行进一步处理。在本文中,我们提出了一种利用2D-DWT实现特征提取技术的算法,并使用提取的系数来表示图像,用于窄杂草和宽杂草的分类。结果表明,提取的2D-DWT系数可以唯一地代表两种不同的杂草类型。
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