On minimum spanning tree streaming for hierarchical segmentation

• Published in: Pattern recognition letters Vol. 138; pp. 155 - 162
• Main Authors: Gigli, Leonardo, Velasco-Forero, Santiago, Marcotegui, Beatriz
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.10.2020; Elsevier Science Ltd; Elsevier
• Subjects: Algorithms; Applications; Artificial Intelligence; Computer Science; Data structures; Discrete Mathematics; Engineering Sciences; Graph theory; Hierarchical segmentation; Image Processing; Image segmentation; Information processing; Machine Learning; Mathematical morphology; Minimum spanning tree; Remote sensing; Signal and Image processing; Statistics; Streaming; Streaming processing; Structural hierarchy; Hierarchical segmentation; Mathematical morphology; Streaming processing; Minimum spanning tree
• ISSN: 0167-8655; 1872-7344
• DOI: 10.1016/j.patrec.2020.07.006

•A new simpler method to produce MST on streaming.•Compare the novel method against the previous one in Gigli et al. (2018).•Show three applications of our algorithm to hierarchical morphological segmentation. The minimum spanning tree (MST) is one the most popular data structure used to extract hierarchical information from images. This work addresses MST construction in streaming for images. First, we focus on the problem of computing a MST of the union of two graphs with a non-empty intersection. Then we show how our solution can be applied to streaming images. The proposed solution relies on the decomposition of the data in two parts. One stable that does not change in the future. This can be stocked or used for further treatments. The other unstable needs further information before becoming stable. The correctness of proposed algorithm has been proven and confirmed in the case of morphological segmentation of remote sensing images.