针对Itti视觉选择性注意模型不具有子特征图显著图归一化过程中权值随任务改变而改变的问题,借鉴自主发育在视觉选择性注意学习的研究成果,提出一种权值可发育视觉选择性注意模型作为图像特征提取的学习机制.该算法采用三层自组织神经网络和Itti视觉选择性注意模型相结合的决策进行寻优,通过对模型的训练学习获取最优权值更新.这样既可以保证在初期特征提取内容的完整性,又降低了系统对不同任务条件的约束性,提高了模型特征提取能力.利用权值可发育视觉选择性注意模型对图像进行感兴趣区域特征提取实验,结果表明,该方法能够提高特征提取准确性、减少运算时间,获得了良好的动态性能.%In the Itti visual selective attention model,the weight does not change as the task changes during the saliency map normalization of child feature map. Therefore,a visual selective attention model with weight development is proposed to be the learning mechanism of image feature extraction by learning from the research achievements of autonomous development in vi-sual selective attention learning. In the algorithm,the strategy of combining three-layer self-organized neural network with Itti vi-sual selective attention model is used for optimization. The optimal weight update is obtained by training and learning of the mod-el,which can not only guarantee the completeness of the initial feature extraction content,but also reduce the constraint of the system on different task conditions,and improve the feature extraction capability of the model. An interested-area feature extrac-tion experiment was carried out for images by using the visual selective attention model with weight development. The results show that the proposed method can improve the accuracy of feature extraction,reduce the computation time,and obtain a good dynamic performance.
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