Article Contents
Article Contents

# Nonlocal regularized CNN for image segmentation

• * Corresponding author: Jun Liu
• Non-local dependency is a very important prior for many image segmentation tasks. Generally, convolutional operations are building blocks that process one local neighborhood at a time which means the convolutional neural networks(CNNs) usually do not explicitly make use of the non-local prior on image segmentation tasks. Though the pooling and dilated convolution techniques can enlarge the receptive field to use some nonlocal information during the feature extracting step, there is no nonlocal priori for feature classification step in the current CNNs' architectures. In this paper, we present a non-local total variation (TV) regularized softmax activation function method for semantic image segmentation tasks. The proposed method can be integrated into the architecture of CNNs. To handle the difficulty of back-propagation for CNNs due to the non-smoothness of nonlocal TV, we develop a primal-dual hybrid gradient method to realize the back-propagation of nonlocal TV in CNNs. Experimental evaluations of the non-local TV regularized softmax layer on a series of image segmentation datasets showcase its good performance. Many CNNs can benefit from our proposed method on image segmentation tasks.

Mathematics Subject Classification: 68U10, 68T07, 65K10.

 Citation:

• Figure 1.  An example of segmentation results by applying the algorithm of [34] and our proposed method on an image from BSD500. When using 4 geometrical nearest neighbors, the weights are set to 1. The segmentation is quite smooth and missing details (Figure 1(b)). When we use Eq. (11) to compute W, the segmentation results are with more details and better accuracy

Figure 2.  Given an input $O$, $\lambda = 3$ and $\tau = 0.03$, we perform algorithms for regularized softmax with local operator and non-local operator, respectively. Figure 2(a) is the convergence of softmax with local operate, the primal energy curve has a peak during the iteration. While in Figure 2(b), the energy curve drops rapidly at first and finally converges smoothly

Figure 3.  Segmentation results predicted by Unet, RUnet and NLUnet on images from testing dataset of White Blood Cell. From row 2 to row 5, The black regions are background, the gray regions are cell sap, the white regions are nucleus

Figure 4.  An enlarged view of segmentation results from Figure 3

Figure 5.  Segmentation results predicted by AUnet, RAUnet and NLAUnet on images from testing dataset of White Blood Cell. From row 2 to row 5, The black regions are background, the gray regions are cell sap, the white regions are nucleus

Figure 6.  Segmentation results predicted by Segnet, RSegnet and NLSegnet trained on CamVid Dataset

Figure 7.  An enlarged view of segmentation results from Figure 6 column 2

Figure 8.  An enlarged view of segmentation results from Figure 6 column 1

Table 1.  Results of Unet, RUnet and NLUnet trained on WBC Dataset

 Method Unet [25] RUnet [10] NLUnet mIoU 89.79 90.15 90.80 Accuracy 97.04 97.13 97.42 RE 1.82 1.30 1.59

Table 2.  Results of AUnet, RAUnet and NLAUnet trained on WBC Dataset

 Method AUnet [23] RAUnet NLAUnet mIoU 90.75 91.01 91.69 Accuracy 97.35 97.40 97.57 RE 1.43 1.41 1.43

Table 3.  Results of Segnet, RSegnet, NLSegnet trained on CamVid Dataset

 Method Segnet [3] RSegnet[10] NLSegnet mIoU 57.35 57.79 59.84 Accuracy 87.74 88.01 88.59 RE 4.10 2.43 3.40
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