Image Segmentation Method Combining Nonlocal Criteria With Graph Cut Theory

Image segmentation, a fundamental problem in image processing, involves distinguishing the foreground from the background. Traditional image segmentation methods are typically divided into local and global approaches. Local methods often result in blurred segmentation due to their reliance on overly localized color sampling models. Conversely, global methods, which depend on the overall color distribution, struggle with images where the foreground and background share similar global characteristics. To overcome these limitations, a novel image segmentation method that integrates nonlocal criteria with graph cut theory is proposed. Initially, the method transforms the image’s color space to the HSV color space, enhancing color contrast. Following this, nonlocal criteria are utilized to compute the data term within the graph cut model, which is then incorporated into the traditional graph cut framework for final segmentation. This approach broadens the neighborhood sampling range in local models, allowing the model to identify more valuable nonlocal samples while mitigating the impact of color similarity in global models. Experimental results across various datasets indicate a marked improvement in segmentation quality achieved by the proposed method.