Annealing and the normalized N-cut

Gedeon, Tomáš; Parker, Albert E.; Campion, Collette; Aldworth, Zane

doi:10.1016/j.patcog.2007.06.014

Cited by 7 publications

(4 citation statements)

References 25 publications

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“…Therefore, how to improve the ability of feature representation is one of the challenges of the current work. The early image segmentation algorithms mainly extract the low-level features of the image for segmentation [10], and the segmentation results often do not contain semantic information. With the development of deep learning, a series of network models based on Convolutional Neural Networks (CNNs) [11] have been proposed successively and have entered a new stage of semantic segmentation.…”

Section: Introductionmentioning

confidence: 99%

SERNet: Squeeze and Excitation Residual Network for Semantic Segmentation of High-Resolution Remote Sensing Images

Zhang

et al. 2022

Remote Sensing

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The semantic segmentation of high-resolution remote sensing images (HRRSIs) is a basic task for remote sensing image processing and has a wide range of applications. However, the abundant texture information and wide imaging range of HRRSIs lead to the complex distribution of ground objects and unclear boundaries, which bring huge challenges to the segmentation of HRRSIs. To solve this problem, in this paper we propose an improved squeeze and excitation residual network (SERNet), which integrates several squeeze and excitation residual modules (SERMs) and a refine attention module (RAM). The SERM can recalibrate feature responses adaptively by modeling the long-range dependencies in the channel and spatial dimensions, which enables effective information to be transmitted between the shallow and deep layers. The RAM pays attention to global features that are beneficial to segmentation results. Furthermore, the ISPRS datasets were processed to focus on the segmentation of vegetation categories and introduce Digital Surface Model (DSM) images to learn and integrate features to improve the segmentation accuracy of surface vegetation, which has certain prospects in the field of forestry applications. We conduct a set of comparative experiments on ISPRS Vaihingen and Potsdam datasets. The results verify the superior performance of the proposed SERNet.

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Section: Introductionmentioning

confidence: 99%

SERNet: Squeeze and Excitation Residual Network for Semantic Segmentation of High-Resolution Remote Sensing Images

Zhang

et al. 2022

Remote Sensing

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“…Early image segmentation algorithms (watershed [6], N-Cut [7], Grab cut [8], etc.) mainly segment an image by extracting its low-level features, and the segmentation results did not contain semantic information.…”

Section: Introductionmentioning

confidence: 99%

Superpixel-Based Attention Graph Neural Network for Semantic Segmentation in Aerial Images

Diao

Zhang

et al. 2022

Remote Sensing

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Semantic segmentation is one of the significant tasks in understanding aerial images with high spatial resolution. Recently, Graph Neural Network (GNN) and attention mechanism have achieved excellent performance in semantic segmentation tasks in general images and been applied to aerial images. In this paper, we propose a novel Superpixel-based Attention Graph Neural Network (SAGNN) for semantic segmentation of high spatial resolution aerial images. A K-Nearest Neighbor (KNN) graph is constructed from our network for each image, where each node corresponds to a superpixel in the image and is associated with a hidden representation vector. On this basis, the initialization of the hidden representation vector is the appearance feature extracted by a unary Convolutional Neural Network (CNN) from the image. Moreover, relying on the attention mechanism and recursive functions, each node can update its hidden representation according to the current state and the incoming information from its neighbors. The final representation of each node is used to predict the semantic class of each superpixel. The attention mechanism enables graph nodes to differentially aggregate neighbor information, which can extract higher-quality features. Furthermore, the superpixels not only save computational resources, but also maintain object boundary to achieve more accurate predictions. The accuracy of our model on the Potsdam and Vaihingen public datasets exceeds all benchmark approaches, reaching 90.23% and 89.32%, respectively.

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“…Graph-based image segmentation has witnessed an explosion of interest in recent years [1][2][3][4][5][6][7][8][9]. However, this image segmentation technique is typically suffers memory overhead and time consuming drawbacks [10].…”

Section: Introductionmentioning

confidence: 99%