This paper addresses a new approach to learn perceptual grouping of the extracted features of the convolutional neural network (CNN) to represent the structure contained in the image. In CNN, the spatial hierarchies between the high-level features are not taken into account. To do so, the perceptual grouping of features is utilized. To consider the intra-relationship between feature maps, modified Guided Co-occurrence Block (mGCoB) is proposed. This block preserves the joint co-occurrence of two features in the spatial domain and it prevents the co-adaptation. Also, to preserve the interrelationship in each feature map, the principle of common region grouping is utilized which states that the features which are located in the same feature map tend to be grouped together. To consider it, an MFC block is proposed. To evaluate the proposed approach, it is applied to some known semantic segmentation and image classification datasets that achieve superior performance.

本文提出了一种新方法，用于学习对卷积神经网络（CNN）提取的特征进行感知分组以表示图像中包含的结构。在CNN中，不考虑高级要素之间的空间层次。为此，利用特征的感知分组。为了考虑特征图之间的内部关系，提出了改进的引导共现块（mGCoB）。该块保留了空间域中两个要素的共同出现，并且阻止了共同适配。另外，为了保持每个特征图中的相互关系，利用了公共区域分组的原理，该原理规定位于同一特征图中的特征倾向于被分组在一起。考虑到这一点，提出了一个MFC块。为了评估提议的方法，