Improving Image Segmentation Accuracy Using Dynamic Performance Analysis of the GrabCut Algorithm

Abstract

. Abstract In this research, a computer vision model was developed that enables object detection, shape identification, and orientation prediction using image segmentation. The GrabCut algorithm was used to segment images. GrabCut was initialized in two ways: first, by initializing the input image with a bounding box (a box surrounding the object’s position in the image), or second, by initializing the input image with a suggested approximation mask (a mask resembling the segmentation). Three steps are performed iteratively: first, the color distribution of the foreground and background is estimated using a mixed Gaussian model (GMM). Second, a random Markov field is generated to manipulate the pixels (foreground/background). Finally, a histogram optimization algorithm is applied to achieve the desired final segmentation. Although the iterative steps may require time and effort to work within the algorithm, GrabCut consistently delivers high-quality results in segmenting the input image and producing an output image that closely resembles the original.