A faster graph-based segmentation algorithm with statistical region merge

Ahmed Fahad, Tim Morris

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The paper presents a modification of a bottom up graph theoretic image segmentation algorithm to improve its performance. This algorithm uses Kruskal's algorithm to build minimum spanning trees for segmentation that reflect global properties of the image: a predicate is defined for measuring the evidence of a boundary between two regions and the algorithm makes greedy decisions to produce the final segmentation. We modify the algorithm by reducing the number of edges required for sorting based on two criteria. We also show that the algorithm produces an over segmented result and suggest a statistical region merge process that will reduce the over segmentation. We have evaluated the algorithm by segmenting various video clips Our experimental results indicate the improved performance and quality of segmentation. © Springer-Verlag Berlin Heidelberg 2006.
Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.
Subtitle of host publication Advances in Visual Computing
PublisherSpringer Nature
Number of pages7
ISBN (Print)978-3-540-48627-5
Publication statusPublished - 2006
Event2nd International Symposium on Visual Computing, ISVC 2006 - Lake Tahoe, NV
Duration: 1 Jul 2006 → …


Conference2nd International Symposium on Visual Computing, ISVC 2006
CityLake Tahoe, NV
Period1/07/06 → …
Internet address


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