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Hierarchy in picture segmentation: a stepwise optimization approach

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2 Author(s)
Beaulieu, J.-M. ; Dept. of Comput. Sci., Laval Univ., Que., Canada ; Goldberg, M.

A segmentation algorithm based on sequential optimization which produces a hierarchical decomposition of the picture is presented. The decomposition is data driven with no restriction on segment shapes. It can be viewed as a tree, where the nodes correspond to picture segments and where links between nodes indicate set inclusions. Picture segmentation is first regarded as a problem of piecewise picture approximation, which consists of finding the partition with the minimum approximation error. Then, picture segmentation is presented as an hypothesis-testing process which merges only segments that belong to the same region. A hierarchical decomposition constraint is used in both cases, which results in the same stepwise optimization algorithm. At each iteration, the two most similar segments are merged by optimizing a stepwise criterion. The algorithm is used to segment a remote-sensing picture, and illustrate the hierarchical structure of the picture.<>

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:11 ,  Issue: 2 )