Learning OpenCV

Mean-Shift Segmentation

In Chapter 5 we introduced the function cvPyrSegmentation(). Pyramid segmentation uses a color merge (over a scale that depends on the similarity of the colors to one another) in order to segment images. This approach is based on minimizing the total energy in the image; here energy is defined by a link strength, which is further defined by color similarity. In this section we introduce cvPyrMeanShiftFiltering(), a similar algorithm that is based on mean-shift clustering over color [Comaniciu99]. We'll see the details of the mean-shift algorithm cvMeanShift() in Chapter 10, when we discuss tracking and motion. For now, what we need to know is that mean shift finds the peak of a color-spatial (or other feature) distribution over time. Here, mean-shift segmentation finds the peaks of color distributions over space. The common theme is that both the motion tracking and the color segmentation algorithms rely on the ability of mean shift to find the modes (peaks) of a distribution.

Given a set of multidimensional data points whose dimensions are (x, y, blue, green, red), mean shift can find the highest density "clumps" of data in this space by scanning a window over the space. Notice, however, that the spatial variables (x, y) can have very different ranges from the color magnitude ranges (blue, green, red). Therefore, mean shift needs to allow for different window radii in different dimensions. In this case we should have one radius for the spatial variables ...