Image segmentation is one of the core task in image processing. Traditionally such operation is performed starting from single pixels requiring a significant amount of computations. Only recently it has been shown that superpixels can be used to improve segmentation performance. In this work we propose a novel superpixel-based hierarchical approach for image segmentation that works by iteratively merging nodes of a weighted undirected graph initialized with the superpixels regions. Proper metrics to drive the regions merging are proposed and experimentally validated using the standard Berkeley Dataset. Our analysis shows that the proposed algorithm runs faster than state of the art techniques while providing accurate segmentation results both in terms of visual and objective metrics.

Fast Superpixel-Based hierarchical approach to image segmentation

VERDOJA, FRANCESCO;GRANGETTO, Marco
2015-01-01

Abstract

Image segmentation is one of the core task in image processing. Traditionally such operation is performed starting from single pixels requiring a significant amount of computations. Only recently it has been shown that superpixels can be used to improve segmentation performance. In this work we propose a novel superpixel-based hierarchical approach for image segmentation that works by iteratively merging nodes of a weighted undirected graph initialized with the superpixels regions. Proper metrics to drive the regions merging are proposed and experimentally validated using the standard Berkeley Dataset. Our analysis shows that the proposed algorithm runs faster than state of the art techniques while providing accurate segmentation results both in terms of visual and objective metrics.
18th International Conference on Image Analysis and Processing, ICIAP 2015
Genoa, Italy
2015
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Springer Verlag
9279
364
374
9783319232300
978-3-319-23231-7
http://springerlink.com/content/0302-9743/copyright/2005/
Bhattacharyya distance; CIEDE2000; Graph partitioning; Hierarchical clustering; Mahalanobis distance; Segmentation; Superpixels; Computer Science (all); Theoretical Computer Science
Verdoja, Francesco; Grangetto, Marco
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1558173
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