Image segmentation based on pixel feature manifold

Image segmentation is an important problem in pattern recognition, computer vision and other related area, which is still a research focus. In this paper, we consider the segmentation as pixel classification scheme and introduce a manifold way to address this problem. Some local features, such as Haar, LBP and SIFT, are used to represent each pixel in the image together with the basic property of the pixel. We put these pixel features on a manifold called pixel feature manifold (PFM) obtained via manifold learning methods and classify pixels with k-NN classifier in the pixel embedding space. Experimental results on MSRC image dataset show that our PFM method can effectively segment images.