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Image mining by spectral features: A case study of scenery image classification

机译:基于光谱特征的图像挖掘:以风景图像分类为例

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摘要

Spectral features of images, such as Gabor filters and wavelet transform can be used for texture image classification. That is, a classifier is trained based on some labeled texture features as the training set to classify unlabeled texture features of images into some pre-defined classes. The aim of this paper is twofold. First, it investigates the classification performance of using Gabor filters, wavelet transform, and their combination respectively, as the texture feature representation of scenery images (such as mountain, castle, etc.). A k-nearest neighbor (k-NN) classifier and support vector machine (SVM) are also compared. Second, three k-NN classifiers and three SVMs are combined respectively, in which each of the combined three classifiers uses one of the above three texture feature representations respectively, to see whether combining multiple classifiers can outperform the single classifier in terms of scenery image classification. The result shows that a single SVM using Gabor filters provides the highest classification accuracy than the other two spectral features and the combined three k-NN classifiers and three SVMs.
机译:图像的光谱特征(例如Gabor滤波器和小波变换)可用于纹理图像分类。即,基于一些标记的纹理特征作为训练集来训练分类器,以将图像的未标记的纹理特征分类为一些预定义的类别。本文的目的是双重的。首先,研究了分别使用Gabor滤镜,小波变换及其组合作为风景图像(如山,城堡等)的纹理特征表示的分类性能。还比较了k最近邻(k-NN)分类器和支持向量机(SVM)。其次,分别组合三个k-NN分类器和三个SVM,其中三个组合的分类器分别使用上述三个纹理特征表示之一,以查看结合多个分类器是否可以胜过单个分类器的风景图像分类。结果表明,使用Gabor滤波器的单个SVM比其他两个频谱特征以及三个k-NN分类器和三个SVM组合提供的分类精度最高。

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