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首页> 外文期刊>International Journal on Computer Science and Engineering >Invariant Moments based War Scene Classification using ANN and SVM: A Comparative Study
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Invariant Moments based War Scene Classification using ANN and SVM: A Comparative Study

机译:基于不变矩的基于ANN和SVM的战争场景分类研究

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In this paper we are trying to classify a war scene from the natural scene. For this purpose two set of image categories are taken viz., opencountry & war tank. By using Invariant Moments features are extracted from the images. The extracted features are trained and tested with (i) Artificial Neural Networks (ANN) using feed forward back propagation algorithm and (ii) Support Vector Machines (SVM) using radial basis kernel function with p=5. The comparative results are proving efficiency of Support Vector Machines towards war scene classification problems by using Invariant Moment feature extraction method. It can be concluded that the proposed work significantly and directly contributes to scene classification and its new applications. The complete work is experimented in Matlab 7.6.0 using real world dataset.
机译:在本文中,我们试图从自然场景中分类战争场景。为此,拍摄了两组图像类别,即开放国家和战争坦克。通过使用不变矩,可以从图像中提取特征。使用(i)使用前馈传播算法的人工神经网络(ANN)和(ii)使用p = 5的径向基核函数的支持向量机(SVM)对提取的特征进行训练和测试。比较结果表明,采用不变矩特征提取方法可以证明支持向量机对战场分类问题的有效性。可以得出的结论是,拟议的工作对场景分类及其新应用具有重大而直接的贡献。使用真实数据集在Matlab 7.6.0中对完整的工作进行了实验。

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