[Bea1989a]
Hierarchy in Picture Segmentation: a Stepwise Optimization Approach

Authors:Beaulieu Jean-Marie, Moris Goldberg

Journal:IEEE Transactions on Pattern Analysis and Machine Intelligence

 1989, vol. 11, issue 2, p. 150–163

ISSN:0162-8828

URL:https://ieeexplore.ieee.org/document/16711

DOI:10.1109/34.16711

Abstract:   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

@Article{Bea1989a,
author = {Beaulieu, Jean-Marie and Goldberg, Moris},
title = {Hierarchy in Picture Segmentation: a Stepwise Optimization Approach},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {11},
number = {2},
pages = {150--163},
year = {1989},
month = {},
abstract = {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},
publisher = {},
url = {https://ieeexplore.ieee.org/document/16711},
issn = {0162-8828},
doi = {10.1109/34.16711},
mypdf = {12},
address = {},
keywords = {computerised picture processing; data structure; hierarchical decomposition; iterative methods; optimisation; picture segmentation; sequential optimization; stepwise optimization; tree; trees (mathematics); nw-05}
}

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INSPEC Accession Number: 3383939
Date of Publication : Feb. 1989
Date of Current Version : 06 août 2002
Issue Date : Feb. 1989
Sponsored by : IEEE Computer Society Publisher: IEEE