Conditional Matting For Post-Segmentation Refinement Segment Anything Model

Abstract

Segment Anything Model (SAM) is a model capable of performing object segmentation in images without requiring any additional training. Although the segmentation produced by SAM lacks high precision, this model holds interesting potential for more accurate segmentation tasks. In this study, we propose a Post-Processing method called Conditional Matting 4 (CM4) to enhance high-precision object segmentation, including prominent, occluded, and complex boundary objects in the segmentation results from SAM. The proposed CM4 Post-Processing method incorporates the use of morphological operations, DistilBERT, InSPyReNet, Grounding DINO, and ViTMatte. We combine these methods to improve the object segmentation produced by SAM. Evaluation is conducted using metrics such as IoU, SAD, MAD, Grad, and Conn. The results of this study show that the proposed CM4 Post-Processing method successfully improves object segmentation with a SAD evaluation score of 20.42 (a 27% improvement from the previous study) and an MSE evaluation score of 21.64 (a 45% improvement from the previous study) compared to the previous research on the AIM-500 dataset. The significant improvement in evaluation scores demonstrates the enhanced capability of CM4 in achieving high precision and overcoming the limitations of the initial segmentation produced by SAM. The contribution of this research lies in the development of an effective CM4 Post-Processing method for enhancing object segmentation in images with high precision. This method holds potential for various computer vision applications that require accurate and detailed object segmentation.