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Supervised vessel segmentation with minimal features

机译:具有最小特征的监督船只分割

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摘要

Current state-of-the art supervised vessel segmentation methods require large number of feature vectors to construct a good model. In this paper, we propose a framework to optimally search for optimal features as inputs to Artificial Neural Network (ANN) trained by Scaled Conjugate Gradient (SCG). SCG is known to speed-up the learning stage in a supervised learning especially when error reduction is critical. The proposed framework is able to reduce features from 16 to 4 dimensions and the overall performance is only decreased by 1% in average.
机译:目前的最先进的船舶分割方法需要大量的特征向量来构建一个好的模型。 在本文中,我们提出了一个框架,以最佳地搜索由缩放共轭梯度(SCG)训练的人工神经网络(ANN)的输入。 已知SCG在监督学习中加速学习阶段,特别是当减少误差至关重要时。 所提出的框架能够将16至4个尺寸的特征减少,平均性能平均下降1%。

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