Scale-Adaptive Conditional Random Fields for Semantic Segmentation

2021 ◽  
Vol 27 (12) ◽  
pp. 574-577
Author(s):  
Jungbeom Lee ◽  
Sungroh Yoon
Keyword(s):  
Random Fields ◽  
Conditional Random Fields ◽  
Semantic Segmentation

scholarly journals Weakly Supervised Conditional Random Fields Model for Semantic Segmentation with Image Patches

2020 ◽  
Vol 10 (5) ◽  
pp. 1679
Author(s):  
Xinying Xu ◽  
Yujing Xue ◽  
Xiaoxia Han ◽  
Zhe Zhang ◽  
Jun Xie ◽  
...  
Keyword(s):  
Random Fields ◽  
Conditional Random Fields ◽  
Semantic Category ◽  
Semantic Segmentation ◽  
Potential Energy Function ◽  
Semantic Class ◽  
Image Patches ◽  
Benchmark Datasets ◽  
Weakly Supervised ◽  
Class Labels

Image semantic segmentation (ISS) is used to segment an image into regions with differently labeled semantic category. Most of the existing ISS methods are based on fully supervised learning, which requires pixel-level labeling for training the model. As a result, it is often very time-consuming and labor-intensive, yet still subject to manual errors and subjective inconsistency. To tackle such difficulties, a weakly supervised ISS approach is proposed, in which the challenging problem of label inference from image-level to pixel-level will be particularly addressed, using image patches and conditional random fields (CRF). An improved simple linear iterative cluster (SLIC) algorithm is employed to extract superpixels. for image segmentation. Specifically, it generates various numbers of superpixels according to different images, which can be used to guide the process of image patch extraction based on the image-level labeled information. Based on the extracted image patches, the CRF model is constructed for inferring semantic class labels, which uses the potential energy function to map from the image-level to pixel-level image labels. Finally, patch based CRF (PBCRF) model is used to accomplish the weakly supervised ISS. Experiments conducted on two publicly available benchmark datasets, MSRC and PASCAL VOC 2012, have demonstrated that our proposed algorithm can yield very promising results compared to quite a few state-of-the-art ISS methods, including some deep learning-based models.

scholarly journals Waterfall Atrous Spatial Pooling Architecture for Efficient Semantic Segmentation

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5361 ◽  
Author(s):  
Bruno Artacho ◽  
Andreas Savakis
Keyword(s):  
Random Fields ◽  
Conditional Random Fields ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Training Time ◽  
Network Parameters ◽  
Spatial Pooling ◽  
Memory Footprint ◽  
Accuracy Increase ◽  
Spatial Pyramid

We propose a new efficient architecture for semantic segmentation, based on a “Waterfall” Atrous Spatial Pooling architecture, that achieves a considerable accuracy increase while decreasing the number of network parameters and memory footprint. The proposed Waterfall architecture leverages the efficiency of progressive filtering in the cascade architecture while maintaining multiscale fields-of-view comparable to spatial pyramid configurations. Additionally, our method does not rely on a postprocessing stage with Conditional Random Fields, which further reduces complexity and required training time. We demonstrate that the Waterfall approach with a ResNet backbone is a robust and efficient architecture for semantic segmentation obtaining state-of-the-art results with significant reduction in the number of parameters for the Pascal VOC dataset and the Cityscapes dataset.

Utilizing Google Images for Training Classifiers in CRF-Based Semantic Segmentation

2016 ◽  
Vol 20 (3) ◽  
pp. 455-461
Author(s):  
Rizki Perdana Rangkuti ◽  
◽  
Vektor Dewanto ◽  
Aprinaldi ◽  
Wisnu Jatmiko ◽  
...  
Keyword(s):  
Random Fields ◽  
Conditional Random Fields ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Training Data ◽  
Learning Performance ◽  
The Internet ◽  
Salient Object ◽  
Annotation Data ◽  
The Given

One promising approach to pixel-wise semantic segmentation is based on conditional random fields (CRFs). CRF-based semantic segmentation requires ground-truth annotations to supervisedly train the classifier that generates unary potentials. However, the number of (public) annotation data for training is limitedly small. We observe that the Internet can provide relevant images for any given keywords. Our idea is to convert keyword-related images to pixel-wise annotated images, then use them as training data. In particular, we rely on saliency filters to identify the salient object (foreground) of a retrieved image, which mostly agrees with the given keyword. We utilize saliency information for back-and-foreground CRF-based semantic segmentation to further obtain pixel-wise ground-truth annotations. Experiment results show that training data from Google images improves both the learning performance and the accuracy of semantic segmentation. This suggests that our proposed method is promising for harvesting substantial training data from the Internet for training the classifier in CRF-based semantic segmentation.

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