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The purpose of this paper is the image segmentation algorithms (ISA) classification analysis, providing for advanced research and design of new computer vision algorithms.

For the development of the required algorithms a three-stage flowchart is suggested. An algorithm of quasi-optimal segmentation is discussed as a possible implementation of the suggested flowchart. A new attribute is introduced reflecting the specific hierarchical algorithm group, which the proposed algorithm belongs to. The introduced attribute refines the overall classification scheme and the requirements for the algorithms under development.

Optimal approximation generation is a computationally intensive task. The computational complexity can be reduced using a hierarchical data framework and a set of auxiliary algorithms, contributing to overall quality improvement. Because hierarchical solutions usually are distinctively suboptimal, further optimization to them was applied. A new classification attribute, proposed in this paper allows to discover previously hidden «blank spots», having decomposed the two-tier ISA classification scheme. The new classification attribute allows to aggregate algorithms, yielding multiple partitions at output and assign them to a dedicated group.

The originality of the paper consists in development of a high-level ISA classification, as well in introduction of a new classification attribute, pertinent to iterative algorithm groups and to hierarchically structured data presentation algorithms.