Optimization of Segmentation Algorithms Through Mean-Shift Filtering Preprocessing

Abstract

This letter proposes an improved mean-shift filtering method. The method is added as a preprocessing step for regional segmentation methods, which aims at benefiting segmentations in a more general way. Using this method, first, a logistic regression model between two edge cues and semantic object boundaries is established. Then, boundary posterior probabilities are predicted by the model and associated with weights in the mean-shift filtering iteration. Finally, the filtered image, instead of the original image, is put into segmentation methods. In experiments, the regression model is trained with an aerial image, which is tested with an aerial image and a QuickBird image. Two popular segmentation methods are adopted for evaluations. Both quantitative and qualitative evaluations reveal that the presented procedure facilitates a superior image segmentation result and higher classification accuracy.