彩色图像特征捆绑的自动模型构建

Construction of the automatic model for feature binding of color image

特征捆绑问题是认知科学和神经科学的中心问题之一。为了对彩色图像中颜色与形状这两种最基本特征实施捆绑,在简化脉冲耦合神经网络(PCNN)模型的基础上,提出一种基于矢量的特征捆绑脉冲耦合神经网络模型。在该模型中,利用神经元的第1次脉冲发放时间将不同的特征进行分离,同时利用神经元的自身输入刺激将属于同一感知对象的不同特征进行捆绑。仿真实验结果表明,该模型能够很好地实现彩色图像特征的分离和捆绑,并实现迭代次数的自动判定,对脉冲耦合神经网络模型在彩色图像特征捆绑的研究和应用中具有一定的参考价值。

The binding problem is regarded as one of the central themes of cognitive science and neuroscience. In order to solve the binding problem of shapes and colors for color images, an advanced vector model for the binding problem, based on the simplified pulse coupled neural network （PCNN）, is presented in this paper. In this model, the first pulse emission time of a neuron separates the different characteristics, while the input stimulus of the neurons itself binds the different characteristics. The experimental results indicate that the model can achieve good feature separation as well as feature bind- ing and it discovers the optimal iteration times. The method has certain reference value for the research and application of the binding problem of color image.