Interactive Image Segmentation Using Multimodal Regularized Kernel Embedding

Interactive image segmentation is a classification problem that involves feature extraction like color features. However, in most cases the color features alone cannot discriminate foreground and background. This is due to the fact that both foreground and background can have possibly overlapping color modalities. Using kernels, different notions of pixel similarities can be incorporated. In this paper, we present an interactive image segmentation approach based on kernel embedding using Kernel Local Fisher Discriminant Analysis (KLFDA). We use KLFDA to transform pixel features into a new discriminative feature space. In this feature space the between-class separability is maximized and the locality within each class is preserved. One difficulty for using KLFDA is the setting for the regularization parameter epsilon. We propose different strategies to overcome this limitation. Our proposed strategies achieve better qualitative and quantitative results compared to state-of-the-art algorithms on the well known ISEG data set for interactive image segmentation.