A fast training approach to artificial neural networks designed for image segmentation

H. Malki, A. Moghaddamjoo
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Abstract

A novel training approach based on the backpropagation algorithm for image segmentation is presented. A set of training vectors is obtained by applying Karhunen-Loeve transformations on the training patterns. Training is started in the direction of the major components and then continues by including other components, in the order of their significance. With this approach, not only will the number of computations during training decrease, but also the problem of trapping in a local minimum will be minimized. This method is applied to image segmentation and compared to the general backpropagation algorithm
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一种用于图像分割的人工神经网络快速训练方法
提出了一种基于反向传播算法的图像分割训练方法。通过对训练模式进行Karhunen-Loeve变换,得到训练向量集。培训从主要组成部分的方向开始,然后按照其重要性的顺序继续包括其他组成部分。这种方法不仅可以减少训练过程中的计算量,而且可以最大限度地减少陷入局部最小值的问题。将该方法应用于图像分割,并与一般的反向传播算法进行了比较
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