IET Image Processing

2013

DOI: 10.1049/iet-ipr.2012.0381

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Pornographic image region detection based on visual attention model in compressed domain

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Generic Semantic Segmentation1

Is Human Skin Blue?1

Introduction1

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Cited by 8 publications

(3 citation statements)

References 18 publications

(32 reference statements)

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“…The SegNet [17] utilise the encoder-decoder structure to obtain feature maps of the same resolution as the input images. Meanwhile, the attention mechanisms are proposed by some methods, such as self-attention [26,27], channel attention [28], and spatial attention [29], to capture information about the area of interest. Liu et al [30] investigate the issue of knowledge distillation for training compact semantic segmentation networks by making use of cumbersome networks.…”

Section: Generic Semantic Segmentationmentioning

confidence: 99%

Multi‐directional feature refinement network for real‐time semantic segmentation in urban street scenes

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et al. 2023

IET Computer Vision

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Efficient and accurate semantic segmentation is crucial for autonomous driving scene parsing. Capturing detailed information and semantic information efficiently through two-branch networks has been widely utilised in real-time semantic segmentation. This study proposes a network named MRFNet based on two-branch strategy to solve the problem of accuracy and speed of segmentation in urban scenes. Many real-time networks do not comprehensively consider contextual information from sub-regions in different directions and at different scales. To handle this problem, a Multi-directional Feature Refinement Module (MFRM) which has three sub-paths to capture information at different scales and directions is proposed. And MFRM reduces computation by using strip pooling and dilated convolution operations. In particular, the authors propose a Feature Cross-guide Aggregation Module to aggregate detailed information and contextual information through the mutual guidance of detailed information and semantic information. This module guides the extraction of feature maps in a more precise direction. Experiments on Cityscapes and CamVid datasets demonstrate the effectiveness of our method by achieving a balance between accuracy and inference speed. Specially, on single 1080Ti GPU, our method yields 78.9% mean intersection over union (mIoU) and 77.4% mIoU at speed of 144.5 frames per second (FPS) and 120.8 FPS on Cityscapes and CamVid datasets respectively.

“…The SegNet [17] utilise the encoder-decoder structure to obtain feature maps of the same resolution as the input images. Meanwhile, the attention mechanisms are proposed by some methods, such as self-attention [26,27], channel attention [28], and spatial attention [29], to capture information about the area of interest. Liu et al [30] investigate the issue of knowledge distillation for training compact semantic segmentation networks by making use of cumbersome networks.…”

Section: Generic Semantic Segmentationmentioning

confidence: 99%

Multi‐directional feature refinement network for real‐time semantic segmentation in urban street scenes

¹

,

²

,

³

et al. 2023

IET Computer Vision

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Efficient and accurate semantic segmentation is crucial for autonomous driving scene parsing. Capturing detailed information and semantic information efficiently through two-branch networks has been widely utilised in real-time semantic segmentation. This study proposes a network named MRFNet based on two-branch strategy to solve the problem of accuracy and speed of segmentation in urban scenes. Many real-time networks do not comprehensively consider contextual information from sub-regions in different directions and at different scales. To handle this problem, a Multi-directional Feature Refinement Module (MFRM) which has three sub-paths to capture information at different scales and directions is proposed. And MFRM reduces computation by using strip pooling and dilated convolution operations. In particular, the authors propose a Feature Cross-guide Aggregation Module to aggregate detailed information and contextual information through the mutual guidance of detailed information and semantic information. This module guides the extraction of feature maps in a more precise direction. Experiments on Cityscapes and CamVid datasets demonstrate the effectiveness of our method by achieving a balance between accuracy and inference speed. Specially, on single 1080Ti GPU, our method yields 78.9% mean intersection over union (mIoU) and 77.4% mIoU at speed of 144.5 frames per second (FPS) and 120.8 FPS on Cityscapes and CamVid datasets respectively.

“…However, this solution (Tables 6 and 7) could be applied in situations where effective and accurate skin detection is required and where, in addition, some wrongly recognised non-skin pixels are accepted. A good example of such approach is the problem of detecting pornographic sites and obscene videos on the Internet [34].…”

Section: Is Human Skin Blue?mentioning

confidence: 99%

Human colour skin detection in CMYK colour space

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2015

IET Image Processing

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“…It can effectively utilize the background information to accurately restore the mask areas. Several works [20][21][22][23][24][25][26] have introduced attention mechanisms to the field of image semantic segmentation to obtain contextual information. Among them, the most commonly used method is to establish a channel semantic dependency model.…”

Section: Introductionmentioning

confidence: 99%

Bilateral attention network for semantic segmentation

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et al. 2021

IET Image Processing

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Enhancing network feature representation capabilities and reducing the loss of image details have become the focus of semantic segmentation task. This work proposes the bilateral attention network for semantic segmentation. The authors embed two attention modules in the encoder and decoder structures . Specifically, high-level features of the encoder structure integrate all channel maps through dense channel relationships learned by the channel correlation coefficient attention module. The positively correlated channels promote each other, and the negatively correlated channels suppress each other. In the decoder structure, low-level features selectively emphasize the edge detail information in the feature map through the position attention module. The feature expression of semantic segmentation is improved by feature fusion of the two attention modules to obtain more accurate segmentation results . Finally, to verify the effectiveness of the model, the authors conduct experiments on the PASCAL VOC 2012 and Cityscapes scene analysis benchmark data sets and achieve a mean intersection-over-union of 74.92% and 66.63%, respectively.

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