Image segmentation is an important and challenging task in image processing. Recently, semi-supervised segmentation methods have received a considerable attention due to their fast and reliable performance. There exist many semi-supervised classification algorithms in machine learning literature such as low density separation (LDS) and Transductive SVM (TSVM). However, most of these are not directly applicable to image segmentation problem due to heavy computational demands. Super pixels substantially reduce the computational requirements of the semi-supervised algorithms, hence, making them applicable to general image segmentation tasks. In this study, we introduce a semi-supervised image segmentation method using machine learning techniques and super pixels. The proposed method yields superior segmentation results over several semi-supervised methods including the popular random walker algorithm. We present experimental evidence suggesting that this interactive image segmentation framework performs well for a broad variety of images.
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