Content-based image classification using a neural network
Content-based image classification using a neural network
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DOI：
10.1016/j.patrec.2003.10.015
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发表时间：
2004-02
期刊：
影响因子：
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通讯作者：
S. Park;Jae Won Lee;Sang-Kyoon Kim
中科院分区：
文献类型：
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作者：
S. Park;Jae Won Lee;Sang-Kyoon Kim
提出了一种基于神经网络的基于内容的图像分类方法。用于分类的图像是可分为前景和背景的对象图像。为了有效地处理目标图像，在预处理阶段，我们使用区域分割技术来提取目标区域。用于分类的特征是从小波变换的图像中提取的基于形状的纹理特征。利用反向传播学习算法构建了特征的神经网络分类器。在各种纹理特征中，对角矩是最有效的。用30个类别的10幅图像组成的300个训练数据和300个测试数据进行测试，分类正确率分别为81.7%和76.7%。
In this paper, we propose a method of content-based image classification using a neural network. The images for classification are object images that can be divided into foreground and background. To deal with the object images efficiently, in the preprocessing step we extract the object region using a region segmentation technique. Features for the classification are shape-based texture features extracted from wavelet-transformed images. The neural network classifier is constructed for the features using the back-propagation learning algorithm. Among the various texture features, the diagonal moment was the most effective. A test with 300 training data and 300 test data composed of 10 images from each of 30 classes shows classification rates of 81.7% and 76.7% correct, respectively.