Consensus-Based Image Segmentation via Topological Persistence

Image segmentation is one of the most important lowlevel operation in image processing and computer vision. It is unlikely for a single algorithm with a fixed set of parameters to segment various images successfully due to variations between images. However, it can be observed that the desired segme...

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Bibliographic Details
Published in:IEEE Computer Society Conference on Computer Vision and Pattern Recognition workshops pp. 1050 - 1057
Main Authors: Qian Ge, Lobaton, Edgar
Format: Conference Proceeding
Language:English
Published: IEEE 01.06.2016
Subjects:
Clustering algorithms
Computer vision
Conferences
Image edge detection
Image segmentation
Robustness
ISSN:2160-7516
Online Access:Get full text
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Summary:Image segmentation is one of the most important lowlevel operation in image processing and computer vision. It is unlikely for a single algorithm with a fixed set of parameters to segment various images successfully due to variations between images. However, it can be observed that the desired segmentation boundaries are often detected more consistently than other boundaries in the output of state of-the-art segmentation results. In this paper, we propose a new approach to capture the consensus of information from a set of segmentations generated by varying parameters of different algorithms. The probability of a segmentation curve being present is estimated based on our probabilistic image segmentation model. A connectivity probability map is constructed and persistent segments are extracted by applying topological persistence to the probability map. Finally, a robust segmentation is obtained with the detection of certain segmentation curves guaranteed. The experiments demonstrate our algorithm is able to consistently capture the curves present within the segmentation set.
ISSN:2160-7516
DOI:10.1109/CVPRW.2016.135