An Interactive Image Segmentation Method Based on Multi-Level Semantic Fusion

Understanding and analyzing 2D/3D sensor data is crucial for a wide range of machine learning-based applications, including object detection, scene segmentation, salient detection. In this context, interactive segmentation vital task in image editing medical diagnosis, involving the accurate separation target from its background based on user annotation information. However, existing methods struggle to effectively leverage such information guide object-segmentation models. To address these challenges, paper proposes an image-segmentation technique static images multi-level semantic fusion. Our method utilizes user-guidance both inside outside segment it image, making applicable 2D 3D data. The proposed introduces cross-stage feature aggregation module, enabling effective propagation multi-scale features previous stages current stage. This mechanism prevents loss caused by multiple upsampling downsampling network, allowing stage make better use Additionally, we incorporate channel attention issue rough network edges. captures richer details level, leading finer experimental evaluation conducted PASCAL Visual Object Classes (VOC) 2012 dataset, our fusion demonstrates intersection over union (IOU) accuracy approximately 2.1% higher than currently popular images. comparative analysis highlights improved performance effectiveness method. Furthermore, exhibits potential applications various fields, imaging robotics. Its compatibility with other learning visual allows integration into workflows. These aspects emphasize significance contributions advancing techniques their practical utility real-world applications.