Join, select, and insert: efficient out-of-core algorithms for hierarchical segmentation trees

TL;DR

This paper introduces efficient out-of-core algorithms for hierarchical segmentation trees, enabling processing of large images by performing select, join, and insert operations without requiring all data in memory.

Contribution

The paper presents three novel algorithms with pseudo-code and complexity analysis for out-of-core computation of binary partition hierarchies.

Findings

Algorithms enable processing of large images beyond main memory capacity

Complexity analysis shows efficiency of the proposed algorithms

Facilitates hierarchical analysis like watershed on large datasets

Abstract

Binary Partition Hierarchies (BPH) and minimum spanning trees are fundamental data structures involved in hierarchical analysis such as quasi-flat zones or watershed. However, classical BPH construction algorithms require to have the whole data in memory, which prevent the processing of large images that cannot fit entirely in the main memory of the computer. To cope with this problem, an algebraic framework leading to a high level calculus was introduced allowing an out-of-core computation of BPHs. This calculus relies on three operations: select, join, and insert. In this article, we introduce three efficient algorithms to perform these operations providing pseudo-code and complexity analysis.