Leaf Image Semantic Segmentation Based on Deep Learning

Wang, Qing; Du, Wei; Ma, Chuan; Gu, Zongyun

doi:10.1109/iciba52610.2021.9688307

Cited by 2 publications

(2 citation statements)

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“…However, the map interpretation is still a difficult task. In recent years, the focus of map interpretation has primarily been on georectification to historical maps (Tavakkol et al., 2019), as well as recognizing specific map features (Chiang et al., 2020) and their semantic information (Wang et al., 2023). Gui et al.…”

Section: Related Workmentioning

confidence: 99%

An ontology‐based semantic description model of ubiquitous map images

Jia,

Yang,

Ding

et al. 2024

Transactions in GIS

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Map images with various themes and cartographic representations have become ubiquitous on the Internet. Such ubiquitously and openly accessible data, named ubiquitous map images in this study, are a potential resource for many geographic information applications such as cartographic design. However, there is a semantic gap between the simple physical form and the complex connotation of ubiquitous map images, which hinders their further applications. To mitigate such barrier, this article develops an ontology‐based semantic description model for ubiquitous map images. First, we discuss the design concerns and principles of the semantic description model of ubiquitous map images. Second, three semantic layers of the semantic description model are proposed, that is, image semantic description layer, cognitive tool layer, and information source layer, and detailed semantic description items are defined for each layer. Furthermore, a formalized semantic description model for ubiquitous map images is developed using ontology construction tools, which lays the foundation for automated and fine‐grained reasoning with the information embedded in map images. We construct a small test dataset consisting of weather maps, and use three types of constraints, namely “time‐topic,” “region‐topic,” and “map auxiliary elements” for the semantic retrieval experiments. The experiments show that the proposed semantic ontology model can enable complex semantic retrieval of ubiquitous map images. Finally, the scalability of the model is discussed from three perspectives: the depth of description, the combination with intelligent methods, and the integration with other open knowledge bases. The proposed model provides a semantic label system for applying data‐driven approaches to decode ubiquitous map images, which also paves the path to the development of cartographic theory in the era of information and communications technologies.

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Section: Related Workmentioning

confidence: 99%

An ontology‐based semantic description model of ubiquitous map images

Jia,

Yang,

Ding

et al. 2024

Transactions in GIS

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“…Semantic segmentation is one of the main research topics in the field of computer vision, and it is a necessary process for computers to recognize the information features of images from the whole to the pixel level. The core of semantic segmentation is to divide images into non-overlapping sub-blocks with different semantic labels based on the information features of image itself, such as gray distribution between image pixels and image texture [1]. Portrait semantic segmentation is an important part of semantic segmentation.…”

Section: Introductionmentioning

confidence: 99%

Portrait Semantic Segmentation Method Based on Dual Modal Information Complementarity

Feng,

Tang

2024

Applied Sciences

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Semantic segmentation of human images is a research hotspot in the field of computer vision. At present, the semantic segmentation models based on U-net generally lack the ability to capture the spatial information of images. At the same time, semantic incompatibility exists because the feature maps of encoder and decoder are directly connected in the skip connection stage. In addition, in low light scenes such as at night, it is easy for false segmentation and segmentation accuracy to appear. To solve the above problems, a portrait semantic segmentation method based on dual-modal information complementarity is proposed. The encoder adopts a double branch structure, and uses a SK-ASSP module that can adaptively adjust the convolution weights of different receptor fields to extract features in RGB and gray image modes respectively, and carries out cross-modal information complementarity and feature fusion. A hybrid attention mechanism is used in the jump connection phase to capture both the channel and coordinate context information of the image. Experiments on human matting dataset show that the PA and MIoU coefficients of this algorithm model reach 96.58% and 94.48% respectively, which is better than U-net benchmark model and other mainstream semantic segmentation models.

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Leaf Image Semantic Segmentation Based on Deep Learning

Cited by 2 publications

References 0 publications

An ontology‐based semantic description model of ubiquitous map images

An ontology‐based semantic description model of ubiquitous map images

Portrait Semantic Segmentation Method Based on Dual Modal Information Complementarity

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