The computer vision consists of image classification, image segmentation, object detection, and tracking, etc. Among them, image segmentation is the most basic technique of the computer vision, which divides an image into foreground and background. This paper proposes an ensemble model using a concept of physical perception for image segmentation. Practically two connected models, the DeepLab and a modified VGG model, get feedback each other in the training process. On inference processing, we combine the results of two parallel models and execute an atrous spatial pyramid pooling (ASPP) and post-processing by using conditional random field (CRF). The proposed model shows better performance than the DeepLab in local area and about 1% improvement on average on comparison of pixel-by-pixel.