Join, select, and insert: efficient out-of-core algorithms for hierarchical segmentation trees
This work addresses a memory bottleneck in hierarchical image segmentation for researchers and practitioners dealing with large-scale image data, representing an incremental improvement over existing methods.
The authors tackled the problem of constructing Binary Partition Hierarchies (BPH) for large images that cannot fit in memory by introducing an algebraic framework with three operations (select, join, insert), and they developed efficient algorithms for these operations with provided pseudo-code and complexity analysis.
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.