Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Image segmentation is a necessary but difficult task in many image processing applications. Unlike conventional auto-segmentation, semi-supervised image segmentation involves a moderate amount of user interaction. , a novel interactive image segmentation method based on diffusion maps is proposed, which can better account for the distribution of pixels in feature space. Our method replaces the pixel features of Euclidean distance with diffusion maps and performs feature optimization and dimensionality reduction in order to achieve a final feature vector, with the assistance of user interaction. On this basis, L0 gradient minimization is applied to achieve steady segmentation results. Finally, the K-means clustering algorithm is applied to perfect the segmentation. Experiments show that our method can obtain better segmentation results than several state-of-the-art interactive image segmentation methods.
['Xun Wang', 'Jianqiu Jin', 'Bailin Yang']
I am writing a research paper on a idea Diffusion map based interactive image segmentation
[{"from": "human", "value": "I am writing a research paper on a idea Diffusion map based interactive image segmentation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Image segmentation is a necessary but difficult task in many image processing applications. Unlike conventional auto-segmentation, semi-supervised image segmentation involves a moderate amount of user interaction. , a novel interactive image segmentation method based on diffusion maps is proposed, which can better account for the distribution of pixels in feature space. Our method replaces the pixel features of Euclidean distance with diffusion maps and performs feature optimization and dimensionality reduction in order to achieve a final feature vector, with the assistance of user interaction. On this basis, L0 gradient minimization is applied to achieve steady segmentation results. Finally, the K-means clustering algorithm is applied to perfect the segmentation. Experiments show that our method can obtain better segmentation results than several state-of-the-art interactive image segmentation methods."}]