Mean Shift: A Robust Approach toward Feature Space Analysis Dorin Comaniciu Peter Meer Siemens Corporate Research 755 College Road East, Princeton, NJ 08540 comanici@scr.siemens.com Electrical and Computer Engineering Department Rutgers University 94 Brett Road, Piscataway, NJ 08854-8058 meer@caip.rutgers.edu Abstract A general nonparametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure, the mean shift. We prove for discrete data the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and thus its utility in detecting the modes of the density. The equivalence of the mean shift procedure to the Nadaraya���Watson estimator from kernel regression and the robust M-estimators of location is also established. Algorithms for two low-level vision tasks, discontinuity preserving smoothing and image segmentation are described as applications. In these algorithms the only user set parameter is the resolution of the analysis, and either gray level or color images are accepted as input. Extensive experimental results illustrate their excellent performance. Keywords: mean shift clustering image segmentation image smoothing feature space low-level vision 1 Introduction Low-level computer vision tasks are misleadingly difficult. Incorrect results can be easily obtained since the employed techniques often rely upon the user correctly guessing the values for the tuning parameters. To improve performance the execution of low-level tasks should be task driven, i.e., supported by independent high level information. This approach, however, requires that first the low-level stage provides a reliable enough representation of the input, and that the feature extrac- tion process is controlled only by very few tuning parameters corresponding to intuitive measures in the input domain. Feature space based analysis of images is a paradigm which can achieve the above stated goals. A feature space is a mapping of the input obtained through the processing of the data in small subsets at a time. For each subset a parametric representation of the feature of interest is 1