Editing Image segmentation (section)

== Histogram-based methods ==

[[Histogram]]-based methods are very efficient compared to other image segmentation methods because they typically require only one pass through the [[pixel]]s. In this technique, a histogram is computed from all of the pixels in the image, and the peaks and valleys in the histogram are used to locate the [[Cluster analysis|clusters]] in the image.<ref name="computervision" /> [[Hue|Color]] or [[Brightness|intensity]] can be used as the measure.

A refinement of this technique is to [[Recursion (computer science)|recursively]] apply the histogram-seeking method to clusters in the image in order to divide them into smaller clusters. This operation is repeated with smaller and smaller clusters until no more clusters are formed.<ref name="computervision" /><ref>{{cite journal | last1 = Ohlander | first1 = Ron | last2 = Price | first2 = Keith | last3 = Reddy | first3 = D. Raj | year = 1978 | title = Picture Segmentation Using a Recursive Region Splitting Method | journal = Computer Graphics and Image Processing | volume = 8 | issue = 3| pages = 313–333 | doi = 10.1016/0146-664X(78)90060-6 }}</ref>

One disadvantage of the histogram-seeking method is that it may be difficult to identify significant peaks and valleys in the image.

Histogram-based approaches can also be quickly adapted to apply to multiple frames, while maintaining their single pass efficiency. The histogram can be done in multiple fashions when multiple frames are considered. The same approach that is taken with one frame can be applied to multiple, and after the results are merged, peaks and valleys that were previously difficult to identify are more likely to be distinguishable. The histogram can also be applied on a per-pixel basis where the resulting information is used to determine the most frequent color for the pixel location. This approach segments based on active objects and a static environment, resulting in a different type of segmentation useful in [[video tracking]].