# GLClick: Interactive Segmentation Combining Global and Local Features
## 2 Related Work  
Interactive segmentation has been a prominent area of research for many years. Approaches in this domain include bounding boxes [13], clicks [14], and scribbles [15], with click-based methods being the most common. This section reviews related work from three perspectives: traditional handcrafted segmentation methods, interactive segmentation with convolutional neural networks (CNNs), and Transformer-based approaches. Traditional Methods. Prior to the widespread adoption of deep learning, interactive segmentation mainly relied on graph-based methods or random walks. [PERSON] et al. [4] introduce the graph cut method, which connects all pixel nodes and assigns energy or weights to these connections. [PERSON] et al. [5] proposed the random walks algorithm in 2006, modeling the image as an undirected graph and solving the Dirichlet problem to achieve segmentation. [PERSON] et al. [6] developed geodesic matting in 2009, which uses a graph-based approach to estimate the kernel density of unknown pixels from labeled points. [PERSON] et al. [7], in 2010, incorporated shape constraints into interactive segmentation, achieving a globally optimal solution. In addition to graph-based and random walk methods, recent studies have explored geometrical particle divide algorithms [16], which are particularly effective in handling imbalanced datasets. These methods, such as the variant of data particle geometrical divide for occupancy detection, offer a promising direction for improving interactive segmentation, especially when dealing with complex spatial distributions [17]. Convolutional Neural Networks. With the advent of large datasets, CNNs have become dominant in high-quality interactive segmentation. [PERSON] et al. [8] introduced deep interactive object selection (DIOS), integrating minimal user input with deep learning to achieve accurate object segmentation. [PERSON] et al. [18] highlight the critical role of the initial click in determining the target object's location and key features. To leverage this, FCA-Net is developed, incorporating a first-click guidance module that processes low-level feature maps and Gaussian maps centered on the initial click to generate segmentation results through convolutional layers. Although fully convolutional networks (FCNs) provide precise segmentation, their upsampling processes often lead to overly smooth outputs, blurring details and degrading image fidelity. Other methods, such as RIS-Net [19], utilize recursive mechanisms and multi-scale information fusion to extract both spatial and semantic features, particularly from remote sensing images. FCANet [20] enhances feature representation by utilizing channel and spatial attention mechanisms, enabling the capture of both local and global image information. BRS [21] improves segmentation accuracy and usability by supporting interactive boundary adjustments,whereas F-BRS [22] refines object boundaries by integrating user feedback with automatic segmentation. FocusCut [23] incrementally refines fine-grained local predictions by cropping patches around user clicks after global segmentation. CDNet [24] propagates user click information via feature and pixel propagation, employing diversified training to reduce optimization ambiguity. MFC-Net [25] utilizes a dual-channel attention module for multi-scale feature cascading, effectively capturing image information at different scales and enhancing the detail and overall consistency of segmentation results. FocalClick [26] achieves efficient segmentation by splitting the inference process into two steps: coarse segmentation followed by localized refinement. Transformer. Since its introduction in 2017, the Transformer model [11] has revolutionized natural language processing (NLP) with its self-attention mechanism and has since been adapted to computer vision. SegFormer [27], one of the early Transformer-based segmentation models, simplifies architectural design while incorporating hybrid attention modules and multi-scale feature fusion, achieving superior accuracy and efficiency.