Guest editorial: Graph learning for computer vision

Many fields in the real world involve a lot of structured data, such as social networks, transportation networks, communication networks etc., and their structures carry important information about the characteristics of the data. However, how to use its structural information to analyse and process the data efficiently has caused continuous research in the field. A graph provides an important means for dealing with structured data. It can describe the geometric structure of data intuitively and flexibly, especially in the representation of spatial irregular data. Graph learning refers to machine learning on graphs, which mainly utilises machine learning algorithms to extract the relevant features of graphs. In recent years, combined with specific applications, researchers have conducted in-depth research on graph learning and proposed various approaches. This Special Issue aims to introduce the latest studies in graph learning for computer vision and proposes new theories and approaches to solve the existing problems. It received a number of submissions from researchers in the field, which all went through a rigorous review process. After several rounds of review, six papers were accepted. These papers cover a variety of fields, such as medicine, remote sensing, and data mining. Specific tasks include image segmentation, knowledge graph reasoning, clustering, and image classification. These accepted papers are mainly divided into two categories. The first category covers the graph learning method guided by optimisation, which obtains the graph structure by establishing a clear model and solving the corresponding optimisation problem. The second category is the deep learning-oriented graph learning method, which combines convolutional neural network and graph neural network to construct the model. In the first paper of the Special Issue by Wang et al. entitled An Enhanced 3D U-Net with Graph-based Refining for Segmentation of Gastrointestinal Stromal Tumours, the authors propose a segmentation algorithm using an improved 3D U-Net to segment gastrointestinal stromal tumours. To enhance information transmission, multiple skip connections are attached into same size feature maps between an encoder and a decoder. Due to difficulties in tumour labelling and other reasons, the author transforms the small intestinal segmentation model into a gastrointestinal stromal tumour segmentation model. Since fully convolutional networks typically suffer from inaccuracies around the boundaries of small structures, the graph neural network is introduced to refine segmentation results. Experiments demonstrate that the proposed method presents superior performance over traditional U-Net. The second paper by Ma et al. entitled Hybrid Attention Mechanism for Few-Shot Relational Learning of Knowledge Graphs, develops a few-shot relationship learning framework. The authors first design an entity-enhanced encoder with weak attention networks and self-attention mechanisms to explore the influence of different levels for source entities. The local graph structure is then utilised to enhance the embedding of the source entity by combining explicit and implicit features. Finally, the model parameters are optimised to infer real entities in the candidate set of similar entities obtained by a loop-processing matching processor. The authors provide extensive experiments and confirm the excellent accuracy of the proposed model. The third paper by Zhao et al. entitled Incremental Multi-View Correlated Feature Learning Based on Non-Negative Matrix Factorization, studies multi-view data. The authors present an incremental multi-view correlated feature learning approach based on non-negative matrix factorization to analyse the uncorrelated items in each view. The algorithm separates uncorrelateditems across views and constructs incremental joint learning with uncorrelated and correlated features to study the common features for multi-view data. Subsequently, the authors design an incremental objective function and derive an effective updating scheme. The proposed method is proved to converge effectively, and its complexity is discussed. They evaluate the proposed solution on real-world datasets and report excellent performance in comparison with the existing state-of-the-art solutions. The fourth paper by Hu et al. entitled Complete/Incomplete Multi-view Subspace Clustering via Soft Block-Diagonal-Induced Regularizer, concentrates on complete and incomplete multi-view clustering problems. The proposed method adopts the self-representation model to individually construct the similarity graphs for each view. To fuse a shared affinity matrix for all views, the authors design the soft block-diagonal-induced regulariser to encourage the generation of a matrix with K diagonal blocks. Considering the incomplete multi-view data, the proposed method effectively utilises some indicator matrices to accurately mark the missing instances in each view. The authors analyse the complexity and convergence of the proposed method on four public datasets and demonstrate that it is better than the most advanced complete/incomplete clustering methods. The fifth paper by Gong et al. entitled Few-shot Learning with Relation Propagation and Constraint, aims to extract valuable information of pair-wise correlation between sparse training samples. The authors state that transductive relation propagation simply propagates the pair-wise relation without relation constraints. Thus, the paper develops a constrained relation–propagation network to capture the accurate relation so as to generate discriminative relational representations. To constrain the pair-wise relation, the proposed method introduces a relation constraint module to regularise the distilled relations between samples, which helps to calibrate the propagated correlation information. Extensive experiments conducted on several benchmark datasets indicate that the proposed method achieves remarkable performance compared to few-shot learning methods. The last paper by Guo et al. entitled CNN-Combined Graph Residual Network with Multilevel Feature Fusion for Hyperspectral Image Classification introduces graph convolutional networks to obtain more superpixel-level features with a topological structure. This paper develops an effective CNN-combined graph residual network with a multilevel feature fusion strategy. The main idea is to learn superpixeltopological information by using the graph residual network and pixel information by using the convolutional neural network. The strategy can adequately leverage the superpixel level and pixel-level features and capture the class boundary features, which further enhances the generalisation performance. Experiments report highly competitive performance in comparison to existing hyperspectral image classification approaches. The papers selected in this Special Issue highlight the extensive study of graph learning in computer vision. We hope that these papers can promote the theoretical study of graph learning as well as provide new ideas for more researchers who are committed to graph learning.

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