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Improving Spider Recognition Based on Biometric Web Analysis

机译:基于生物特征网络分析的蜘蛛识别

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This work presents an improvement of the automatic and supervised spider identification approach based on biometric spider web analysis. We have used as feature extractor, a Joint Approximate Diagonalization of Eigen-matrixes Independent Component Analysis applying to a binary image with a reduced size (20x20 pixels) from the colour original image. Finally, we have applied a least square support vector machine as classifier, reaching over 98.15% in our hold-50%-out validation. This system is making easier Biologists' tasks in this field, because they can have a second opinion or have a tool for this work.
机译:这项工作提出了基于生物特征蜘蛛网分析的自动监督蜘蛛识别方法的改进。我们已将特征矩阵的联合近似对角化(特征矩阵独立分量分析)用作特征提取器,该联合近似对角化应用于具有彩色原始图像的缩小尺寸(20x20像素)的二进制图像。最后,我们应用了最小二乘支持向量机作为分类器,在我们的50%外延验证中达到了98.15%以上。该系统使生物学家在该领域的任务变得更容易,因为他们可以对此有第二意见或拥有进行这项工作的工具。

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