Assigning meaning to parts of image data is the goal of semantic image segmentation. Machine learning methods, specifically supervised learning is commonly used in a variety of tasks formulated as semantic segmentatio...
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Assigning meaning to parts of image data is the goal of semantic image segmentation. Machine learning methods, specifically supervised learning is commonly used in a variety of tasks formulated as semanticsegmentation. One of the major challenges in the supervised learning approaches is expressing and collecting the rich knowledge that experts have with respect to the meaning present in the image data. Towards this, typically a fixed set of labels is specified and experts are tasked with annotating the pixels, patches or segments in the images with the given labels. In general, however, the set of classes does not fully capture the rich semantic information present in the images. For example, in medical imaging such as histology images, the different parts of cells could be grouped and sub-grouped based on the expertise of the pathologist. To achieve such a precise semantic representation of the concepts in the image, we need access to the full depth of knowledge of the annotator. In this work, we develop a novel approach to collect segmentation annotations from experts based on psychometric testing. Our method consists of the psychometric testing procedure, active query selection, query enhancement, and a deep metric learning model to achieve a patch-level image embedding that allows for semanticsegmentation of images. We show the merits of our method with evaluation on the synthetically generated image, aerial image and histology image.
Land cover mapping is crucial for natural resource assessment, urban planning, and sustainable development. Land cover nomenclature often includes two or three hierarchical levels with tree-like hierarchical structure...
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Land cover mapping is crucial for natural resource assessment, urban planning, and sustainable development. Land cover nomenclature often includes two or three hierarchical levels with tree-like hierarchical structures. This study aims to explore these hierarchical relationships and the potential of hierarchical semantic segmentation for land cover mapping. We propose a hierarchical semantic segmentation architecture by taking advantage of dual U-shaped network, named as HierU-Net. The coarse-level result is ingested to the fine-level segmentation functioned as soft constraints. The propagation of error will not be certain. Moreover, we employ a multitask loss function weighted by homoscedastic uncertainty to optimize the training. To evaluate the performance of the proposed method, we create a hierarchical semantic segmentation dataset (HierToulouse), which contains 11 528 samples, including images and land cover labels at two hierarchical levels. The experiments demonstrate that the proposed approach is capable of achieving accurate land cover segmentation at both coarse and fine levels, with segmentation results surpassing those obtained using the flat method.
semanticsegmentation is one of the most important tasks in the field of remote sensing. As the spatial resolution increases, the remote sensing images can capture more detailed information and make hierarchical seman...
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semanticsegmentation is one of the most important tasks in the field of remote sensing. As the spatial resolution increases, the remote sensing images can capture more detailed information and make hierarchicalsemantic interpretation possible. However, hierarchical semantic segmentation encounters high heterogeneity not only within the intra-layer classes but also among inter-layer classes. It brings challenges to semanticsegmentation methods such as the convolutional neural network (CNN). In this article, a hierarchical self-learning knowledge inference model (HSKIM) based on the Markov random field (MRF) model is proposed for hierarchical semantic segmentation of remote sensing images. The HSKIM model introduces a new framework that integrates the advantages of CNN-based data feature learning and MRF-based hierarchicalsemantic inference. It contains three modules: data learning module ( $\boldsymbol {D}$ ), inference units generation module ( $\boldsymbol {I}$ ), and self-learning knowledge inference module ( $\boldsymbol {S}$ ). The module $\boldsymbol {D}$ uses CNN to learn specific data features layer by layer and extract preliminary geographical objects as the initial results. The module I refines the geographical objects using a novel boundary-preservation trick to generate more accurate inference units with clear geographical meaning. The module S introduces a hierarchical object-based MRF model to implement semantic inference among intra-layer and inter-layer inference units, guided by the spatial interactions and geographical criteria. This module can self-learn and update the relationship between classes iteratively and provide the final result. Experiments on the GID dataset with hierarchical classes, alongside 12 state-of-the-art CNN-based methods, validate the effectiveness and robustness of the proposed HSKIM model. The code of this article is available at https://***/iichengzi/HSKIM.
Aiming to address the issue of low accuracy in existing algorithms due to the limited scale of specific apple disease datasets and complex background information, a deep learning method integrating apple disease class...
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ISBN:
(纸本)9798400709234
Aiming to address the issue of low accuracy in existing algorithms due to the limited scale of specific apple disease datasets and complex background information, a deep learning method integrating apple disease classification and segmentation is proposed. Initially, the method employs the CycleGAN network for data enhancement to combat overfitting problems in deep learning. Secondly, it constructs a deep residual disease classification network embedded in the convolutional block attention module. In essence, the ResNet50(*) module extracts fine-grained visual features and incorporates both the channel attention mechanism and spatial attention mechanism to focus on two critical features, thereby enhancing the disease classification performance of the model across various scenarios. Lastly, a hierarchical semantic segmentation model is introduced to achieve a more accurate segmentation effect through segmented segmentation. Experimental results demonstrate that the proposed method achieves an accuracy of 97.82% on the apple disease classification task, while obtaining MPA and MIoU scores of 94.85% and 91.75%, respectively, on the segmentation task.
Quick and accurate detection of insulator defects from the complex aerial background (such as trees, hillsides, lakes, and buildings) is important work to ensure the safe operation of transmission lines. The existing ...
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Quick and accurate detection of insulator defects from the complex aerial background (such as trees, hillsides, lakes, and buildings) is important work to ensure the safe operation of transmission lines. The existing detection methods have difficulty detecting the defect target due to the strong interference of complex backgrounds in aerial images. To solve this problem, we propose an insulator defect detection model based on a cascaded network. First, we introduce a hierarchical semantic segmentation network to separate the complex background from the target insulator, which is embedded into the main feature extraction branch to form a "segmentation-detection" cascade network to solve the interference problem of complex background when extracting target information;Second, aiming at the problem of direct fusion of conflicting information in different feature layers in the bi-directional path aggregation neck structure in the detection network, we propose an across-scale feature pyramid with feature refinement structure to enhance the information characteristics of insulator defect targets and improve the multi-scale expression ability of the network. Then, aiming at the problem of difficult samples and imbalance of positive and negative samples in the process of target detection, we propose a focal shape intersection over union loss (focal-SIOU-loss), which improves the precision and stability of the regression process by introducing the weight adjustment mechanism of focal loss and the structural similarity of SIOU Loss. Finally, the experimental results show that, compared with the standard detection models such as YOLOv5, YOLO7, and YOLOv8, the proposed detection model achieves a better performance in the precision, recall rate, and robustness in detecting insulator defects under complex backgrounds. (c) 2024 SPIE and IS&T
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