Image Segmentation Using Hierarchical Merge Tree | IEEE Journals & Magazine | IEEE Xplore

Image Segmentation Using Hierarchical Merge Tree


Abstract:

This paper investigates one of the most fundamental computer vision problems: image segmentation. We propose a supervised hierarchical approach to object-independent imag...Show More

Abstract:

This paper investigates one of the most fundamental computer vision problems: image segmentation. We propose a supervised hierarchical approach to object-independent image segmentation. Starting with oversegmenting superpixels, we use a tree structure to represent the hierarchy of region merging, by which we reduce the problem of segmenting image regions to finding a set of label assignment to tree nodes. We formulate the tree structure as a constrained conditional model to associate region merging with likelihoods predicted using an ensemble boundary classifier. Final segmentations can then be inferred by finding globally optimal solutions to the model efficiently. We also present an iterative training and testing algorithm that generates various tree structures and combines them to emphasize accurate boundaries by segmentation accumulation. Experiment results and comparisons with other recent methods on six public data sets demonstrate that our approach achieves the state-of-the-art region accuracy and is competitive in image segmentation without semantic priors.
Published in: IEEE Transactions on Image Processing ( Volume: 25, Issue: 10, October 2016)
Page(s): 4596 - 4607
Date of Publication: 18 July 2016

ISSN Information:

PubMed ID: 27448353

Funding Agency:


References

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