Learning Multi-level Region Consistency with Dense Multi-label Networks for Semantic Segmentation

Semantic image segmentation is a fundamental task in image understanding. Per-pixel semantic labelling of an image benefits greatly from the ability to consider region consistency both locally and globally. However, many Fully Convolutional Network based methods do not impose such consistency, which may give rise to noisy and implausible predictions. We address this issue by proposing a dense multi-label network module that is able to encourage the region consistency at different levels. This simple but effective module can be easily integrated into any semantic segmentation systems. With comprehensive experiments, we show that the dense multi-label can successfully remove the implausible labels and clear the confusion so as to boost the performance of semantic segmentation systems.

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Multi-level graph convolutional recurrent neural network for semantic image segmentation

Telecommunication Systems ◽

10.1007/s11235-021-00769-y ◽

2021 ◽

Author(s):

Dingchao Jiang ◽

Hua Qu ◽

Jihong Zhao ◽

Jianlong Zhao ◽

Wei Liang

Keyword(s):

Neural Network ◽

Image Segmentation ◽

Recurrent Neural Network ◽

Semantic Image Segmentation ◽

Multi Level

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Parallel Fully Convolutional Network for Semantic Segmentation

IEEE Access ◽

10.1109/access.2020.3042254 ◽

2021 ◽

Vol 9 ◽

pp. 673-682

Author(s):

Jian Ji ◽

Xiaocong Lu ◽

Mai Luo ◽

Minghui Yin ◽

Qiguang Miao ◽

...

Keyword(s):

Semantic Segmentation ◽

Convolutional Network ◽

Fully Convolutional Network

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High-accuracy image segmentation for lactating sows using a fully convolutional network

Biosystems Engineering ◽

10.1016/j.biosystemseng.2018.10.005 ◽

2018 ◽

Vol 176 ◽

pp. 36-47 ◽

Cited By ~ 5

Author(s):

Aqing Yang ◽

Huasheng Huang ◽

Chan Zheng ◽

Xunmu Zhu ◽

Xiaofan Yang ◽

...

Keyword(s):

Image Segmentation ◽

High Accuracy ◽

Convolutional Network ◽

Fully Convolutional Network ◽

Lactating Sows

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Outdoor Scenes Pixel-wise Semantic Segmentation using Polarimetry and Fully Convolutional Network

Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications ◽

10.5220/0007360203280335 ◽

2019 ◽

Cited By ~ 2

Author(s):

Marc Blanchon ◽

Olivier Morel ◽

Yifei Zhang ◽

Ralph Seulin ◽

Nathan Crombez ◽

...

Keyword(s):

Semantic Segmentation ◽

Convolutional Network ◽

Fully Convolutional Network ◽

Outdoor Scenes

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Semantic Image Segmentation with Deep Convolutional Neural Networks and Quick Shift

Symmetry ◽

10.3390/sym12030427 ◽

2020 ◽

Vol 12 (3) ◽

pp. 427 ◽

Cited By ~ 1

Author(s):

Sanxing Zhang ◽

Zhenhuan Ma ◽

Gang Zhang ◽

Tao Lei ◽

Rui Zhang ◽

...

Keyword(s):

Neural Networks ◽

Image Segmentation ◽

Convolutional Neural Networks ◽

Semantic Segmentation ◽

Autonomous Driving ◽

Input Image ◽

Feature Representation ◽

Segmentation Algorithm ◽

Deep Convolutional Neural Networks ◽

Semantic Image Segmentation

Semantic image segmentation, as one of the most popular tasks in computer vision, has been widely used in autonomous driving, robotics and other fields. Currently, deep convolutional neural networks (DCNNs) are driving major advances in semantic segmentation due to their powerful feature representation. However, DCNNs extract high-level feature representations by strided convolution, which makes it impossible to segment foreground objects precisely, especially when locating object boundaries. This paper presents a novel semantic segmentation algorithm with DeepLab v3+ and super-pixel segmentation algorithm-quick shift. DeepLab v3+ is employed to generate a class-indexed score map for the input image. Quick shift is applied to segment the input image into superpixels. Outputs of them are then fed into a class voting module to refine the semantic segmentation results. Extensive experiments on proposed semantic image segmentation are performed over PASCAL VOC 2012 dataset, and results that the proposed method can provide a more efficient solution.

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TISNet-Enhanced Fully Convolutional Network with Encoder-Decoder Structure for Tongue Image Segmentation in Traditional Chinese Medicine

Computational and Mathematical Methods in Medicine ◽

10.1155/2020/6029258 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Xiaodong Huang ◽

Hui Zhang ◽

Li Zhuo ◽

Xiaoguang Li ◽

Jing Zhang

Keyword(s):

Image Segmentation ◽

Receptive Field ◽

Positional Information ◽

Dice Similarity Coefficient ◽

Segmentation Method ◽

Feature Maps ◽

Convolutional Network ◽

Fully Convolutional Network ◽

Segmentation Methods ◽

Feature Pyramid

Extracting the tongue body accurately from a digital tongue image is a challenge for automated tongue diagnoses, as the blurred edge of the tongue body, interference of pathological details, and the huge difference in the size and shape of the tongue. In this study, an automated tongue image segmentation method using enhanced fully convolutional network with encoder-decoder structure was presented. In the frame of the proposed network, the deep residual network was adopted as an encoder to obtain dense feature maps, and a Receptive Field Block was assembled behind the encoder. Receptive Field Block can capture adequate global contextual prior because of its structure of the multibranch convolution layers with varying kernels. Moreover, the Feature Pyramid Network was used as a decoder to fuse multiscale feature maps for gathering sufficient positional information to recover the clear contour of the tongue body. The quantitative evaluation of the segmentation results of 300 tongue images from the SIPL-tongue dataset showed that the average Hausdorff Distance, average Symmetric Mean Absolute Surface Distance, average Dice Similarity Coefficient, average precision, average sensitivity, and average specificity were 11.2963, 3.4737, 97.26%, 95.66%, 98.97%, and 98.68%, respectively. The proposed method achieved the best performance compared with the other four deep-learning-based segmentation methods (including SegNet, FCN, PSPNet, and DeepLab v3+). There were also similar results on the HIT-tongue dataset. The experimental results demonstrated that the proposed method can achieve accurate tongue image segmentation and meet the practical requirements of automated tongue diagnoses.

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Fully convolutional network for rice seedling and weed image segmentation at the seedling stage in paddy fields

PLoS ONE ◽

10.1371/journal.pone.0215676 ◽

2019 ◽

Vol 14 (4) ◽

pp. e0215676 ◽

Cited By ~ 15

Author(s):

Xu Ma ◽

Xiangwu Deng ◽

Long Qi ◽

Yu Jiang ◽

Hongwei Li ◽

...

Keyword(s):

Image Segmentation ◽

Rice Seedling ◽

Seedling Stage ◽

Paddy Fields ◽

Convolutional Network ◽

Fully Convolutional Network

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Class-Wise Fully Convolutional Network for Semantic Segmentation of Remote Sensing Images

Remote Sensing ◽

10.3390/rs13163211 ◽

2021 ◽

Vol 13 (16) ◽

pp. 3211

Author(s):

Tian Tian ◽

Zhengquan Chu ◽

Qian Hu ◽

Li Ma

Keyword(s):

Remote Sensing ◽

Image Interpretation ◽

Semantic Segmentation ◽

Remote Sensing Images ◽

Feature Maps ◽

Convolutional Network ◽

Fully Convolutional Network ◽

Semantic Labeling ◽

Benchmark Datasets ◽

Semantic Label

Semantic segmentation is a fundamental task in remote sensing image interpretation, which aims to assign a semantic label for every pixel in the given image. Accurate semantic segmentation is still challenging due to the complex distributions of various ground objects. With the development of deep learning, a series of segmentation networks represented by fully convolutional network (FCN) has made remarkable progress on this problem, but the segmentation accuracy is still far from expectations. This paper focuses on the importance of class-specific features of different land cover objects, and presents a novel end-to-end class-wise processing framework for segmentation. The proposed class-wise FCN (C-FCN) is shaped in the form of an encoder-decoder structure with skip-connections, in which the encoder is shared to produce general features for all categories and the decoder is class-wise to process class-specific features. To be detailed, class-wise transition (CT), class-wise up-sampling (CU), class-wise supervision (CS), and class-wise classification (CC) modules are designed to achieve the class-wise transfer, recover the resolution of class-wise feature maps, bridge the encoder and modified decoder, and implement class-wise classifications, respectively. Class-wise and group convolutions are adopted in the architecture with regard to the control of parameter numbers. The method is tested on the public ISPRS 2D semantic labeling benchmark datasets. Experimental results show that the proposed C-FCN significantly improves the segmentation performances compared with many state-of-the-art FCN-based networks, revealing its potentials on accurate segmentation of complex remote sensing images.

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