# GLClick: Interactive Segmentation Combining Global and Local Features
## 4 Experiment
### Comparison with Other Methods  
We compare two categories of algorithms: CNN-based and Transformer-based interactive image segmentation methods. The CNN-based algorithms include deep interactive object selection (DIOS) [8] and FCA-Net [18], both of which utilize fully convolutional networks. Additional CNN-based methods included in our comparison are latent diversity-based segmentation (LD) [43], back-propagating refinement scheme (BRS) [21], f-BRS [22], RITM [30], CDNet [24], and MFC-Net [25], as well as PseudoClick [44], FocalClick [26], and FocusCut [23]. The Transformer-based methods included in our comparison are SimpleClick [9], FCA-Net with iterative click loss (ICL) [18], segment anything module (SAM) [45], and standard inference (StdInfer) [30]. In this experiment, the metrics mNoC@85 and mNoC@90 represent the mean number of clicks required to achieve an mIoU of 85% and 90%, respectively. Table 1 shows the mNoC values for various algorithms across four datasets. Our proposed method demonstrates superior performance across all datasets. Specifically, on the Berkeley dataset, it reduces the number of clicks by 0.33 at 90% IoU, achieving a 13.4% improvement over the baseline. On the DAVIS dataset, it reduces the number of clicks by 0.48 at 90% IoU, resulting in an 8.8% improvement. These enhancements stem from integrating the ResNet50 module with the baseline, which improves feature representation by combining local perception with global context, thereby significantly reducing the user interaction burden. To validate the performance of our proposed model, we compare it with the SimpleClick baseline using the ViT-Base backbone, where our approach consistently achieves superior results across multiple datasets. We also trained on ViT-Large, which further demonstrates the effectiveness of our model. Although hardware limitations prevented us from training on ViT-Huge, our model still outperforms other state-of-the-art methods that utilize larger backbones, indicating its robustness and efficiency. The comparison is enriched by including algorithms such as ICL, SAM, and StdInfer, all of which utilize ViT-H as their backbone. Notably, our algorithm, despite using ViT-Base and ViT-Large, consistently outperforms these methods across most datasets. This performance can be attributed to our algorithm's effective integration of global and local features, enhancing its overall capability. As highlighted in Table 1, our proposed method not only achieves a lower mean number of clicks but also maintains a strong balance between accuracy and user interaction efficiency. This showcases the practical implications of our approach for real-world interactive segmentation tasks, where minimizing user input while maximizing output quality is essential. Figure 5 illustrates the mean intersection over union (mIoU) against the number of clicks, providing an intuitive visualization of the performance of various algorithms in interactive image segmentation. This graph highlights the performance differences among algorithms and the advantages of our proposed GCLick algorithm. As anticipated, the mIoU increases with the number of clicks for all algorithms, as additional user interactions generally lead to more accurate segmentation outcomes. The red line in the figure represents the performance of our GCLick algorithm. This line consistently stays above the curves of other algorithms for the same number of clicks, particularly on the DAVIS dataset, highlighting GCLick's effectiveness in enhancing segmentation accuracy. Notably, GCLick requires fewer interaction clicks to achieve the same or higher mIoU levels compared to other algorithms, as illustrated in Figure 5. This advantage underscores GLClick's role in optimizing user interaction by alleviating operational burden and demonstrates its robust capability in addressing complex image segmentation tasks.