Image semantic segmentation optimization by Conditional Random Field integrated with object clique potential

2017 ◽  
Author(s):  
Jingwei Li ◽  
Hua Yang ◽  
Lin Chen ◽  
Cheng Zhi
Keyword(s):  
Random Field ◽  
Conditional Random Field ◽  
Semantic Segmentation

scholarly journals EFFICIENT SEMANTIC SEGMENTATION OF MAN-MADE SCENES USING FULLY-CONNECTED CONDITIONAL RANDOM FIELD

2016 ◽  
Vol XLI-B3 ◽  
pp. 633-640
Author(s):  
Weihao Li ◽  
Michael Ying Yang
Keyword(s):  
Random Field ◽  
State Of The Art ◽  
Conditional Random Field ◽  
Contextual Information ◽  
Mean Field ◽  
Semantic Segmentation ◽  
Gaussian Kernels ◽  
Previous State ◽  
Fully Connected ◽  
Better Than

In this paper we explore semantic segmentation of man-made scenes using fully connected conditional random field (CRF). Images of man-made scenes display strong contextual dependencies in the spatial structures. Fully connected CRFs can model long-range connections within the image of man-made scenes and make use of contextual information of scene structures. The pairwise edge potentials of fully connected CRF models are defined by a linear combination of Gaussian kernels. Using filter-based mean field algorithm, the inference is very efficient. Our experimental results demonstrate that fully connected CRF performs better than previous state-of-the-art approaches on both eTRIMS dataset and LabelMeFacade dataset.

scholarly journals Boundary Perception Guidance: A Scribble-Supervised Semantic Segmentation Approach

2019 ◽  
Author(s):  
Bin Wang ◽  
Guojun Qi ◽  
Sheng Tang ◽  
Tianzhu Zhang ◽  
Yunchao Wei ◽  
...  
Keyword(s):  
Random Field ◽  
State Of The Art ◽  
Conditional Random Field ◽  
Semantic Segmentation ◽  
Semantic Feature ◽  
Feature Maps ◽  
Feature Enhancement ◽  
Pascal Voc ◽  
Segmentation Approach ◽  
Fully Connected

Semantic segmentation suffers from the fact that densely annotated masks are expensive to obtain. To tackle this problem, we aim at learning to segment by only leveraging scribbles that are much easier to collect for supervision. To fully explore the limited pixel-level annotations from scribbles, we present a novel Boundary Perception Guidance (BPG) approach, which consists of two basic components, i.e., prediction refinement and boundary regression. Specifically, the prediction refinement progressively makes a better segmentation by adopting an iterative upsampling and a semantic feature  enhancement strategy. In the boundary regression, we employ class-agnostic edge maps for supervision to effectively guide the segmentation network in localizing the boundaries between different semantic regions, leading to producing finer-grained representation of feature maps for semantic segmentation. The experiment results on the PASCAL VOC 2012 demonstrate the proposed BPG achieves mIoU of 73.2% without fully connected Conditional Random Field (CRF) and 76.0% with CRF, setting up the new state-of-the-art in literature.

Superpixel based continuous conditional random field neural network for semantic segmentation

2019 ◽  
Vol 340 ◽  
pp. 196-210 ◽  
Author(s):  
Lei Zhou ◽  
Keren Fu ◽  
Zhi Liu ◽  
Fan Zhang ◽  
Zhimin Yin ◽  
...  
Keyword(s):  
Neural Network ◽  
Random Field ◽  
Conditional Random Field ◽  
Semantic Segmentation

scholarly journals Affiliated Fusion Conditional Random Field for Urban UAV Image Semantic Segmentation

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 993
Author(s):  
Yingying Kong ◽  
Bowen Zhang ◽  
Biyuan Yan ◽  
Yanjuan Liu ◽  
Henry Leung ◽  
...  
Keyword(s):  
Random Field ◽  
Conditional Random Field ◽  
Region Of Interest ◽  
Semantic Segmentation ◽  
Geographical Information ◽  
Aerial Image ◽  
Surface Model ◽  
Uav Images ◽  
Uav Image ◽  
Multiple Metrics

Unmanned aerial vehicles (UAV) have had significant progress in the last decade, which is applied to many relevant fields because of the progress of aerial image processing and the convenience to explore areas that men cannot reach. Still, as the basis of further applications such as object tracking and terrain classification, semantic image segmentation is one of the most difficult challenges in the field of computer vision. In this paper, we propose a method for urban UAV images semantic segmentation, which utilizes the geographical information of the region of interest in the form of a digital surface model (DSM). We introduce an Affiliated Fusion Conditional Random Field (AF-CRF), which combines the information of visual pictures and DSM, and a multi-scale strategy with attention to improve the segmenting results. The experiments show that the proposed structure performs better than state-of-the-art networks in multiple metrics.

scholarly journals An End-to-End Oil-Spill Monitoring Method for Multisensory Satellite Images Based on Deep Semantic Segmentation

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 725 ◽  
Author(s):  
Yantong Chen ◽  
Yuyang Li ◽  
Junsheng Wang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Random Field ◽  
Oil Spill ◽  
Conditional Random Field ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Mean Field Approximation ◽  
Monitoring Method ◽  
End To End

In remote-sensing images, a detected oil-spill area is usually affected by spot noise and uneven intensity, which leads to poor segmentation of the oil-spill area. This paper introduced a deep semantic segmentation method that combined a deep-convolution neural network with the fully connected conditional random field to form an end-to-end connection. On the basis of Resnet, it first roughly segmented a multisource remote-sensing image as input by the deep convolutional neural network. Then, we used the Gaussian pairwise method and mean-field approximation. The conditional random field was established as the output of the recurrent neural network. The oil-spill area on the sea surface was monitored by the multisource remote-sensing image and was estimated by optical image. We experimentally compared the proposed method with other models on the dataset established by the multisensory satellite image. Results showed that the method improved classification accuracy and captured fine details of the oil-spill area. The mean intersection over the union was 82.1%, and the monitoring effect was obviously improved.

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