  In this paper, we present an algorithm to automatically detect meaningful
modes in a histogram. The proposed method is based on the behavior of local
minima in a scale-space representation. We show that the detection of such
meaningful modes is equivalent in a two classes clustering problem on the
length of minima scale-space curves. The algorithm is easy to implement, fast,
and does not require any parameters. We present several results on histogram
and spectrum segmentation, grayscale image segmentation and color image
reduction.
