Article

Spatial distance dependent Chinese restaurant processes for image segmentation

Authors:

Andrei B. Ungureanu,

Erik B. Sudderth,

David M. BleiAuthors Info & Claims

NIPS'11: Proceedings of the 25th International Conference on Neural Information Processing Systems

Pages 1476 - 1484

Published: 12 December 2011 Publication History

Abstract

The distance dependent Chinese restaurant process (ddCRP) was recently introduced to accommodate random partitions of non-exchangeable data [1]. The dd-CRP clusters data in a biased way: each data point is more likely to be clustered with other data that are near it in an external sense. This paper examines the dd-CRP in a spatial setting with the goal of natural image segmentation. We explore the biases of the spatial ddCRP model and propose a novel hierarchical extension better suited for producing "human-like" segmentations. We then study the sensitivity of the models to various distance and appearance hyperparameters, and provide the first rigorous comparison of nonparametric Bayesian models in the image segmentation domain. On unsupervised image segmentation, we demonstrate that similar performance to existing nonparametric Bayesian models is possible with substantially simpler models and algorithms.

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Cited By

Nakano MFujiwara YKimura AYamada TUeda NRanzato MBeygelzimer ADauphin YLiang PVaughan J(2021)Permuton-induced Chinese restaurant processProceedings of the 35th International Conference on Neural Information Processing Systems10.5555/3540261.3542382(27695-27708)Online publication date: 6-Dec-2021
https://dl.acm.org/doi/10.5555/3540261.3542382
Zhang BZhang LGuo TWang YChen FGuo YFarooq F(2018)Simultaneous Urban Region Function Discovery and Popularity Estimation via an Infinite Urbanization Process ModelProceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3219819.3219987(2692-2700)Online publication date: 19-Jul-2018
https://dl.acm.org/doi/10.1145/3219819.3219987
Mitra A(2017)Bayesian approach to Spatio-temporally Consistent Simulation of Daily Monsoon Rainfall over IndiaProceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems10.1145/3139958.3139975(1-4)Online publication date: 7-Nov-2017
https://dl.acm.org/doi/10.1145/3139958.3139975
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Index Terms

Spatial distance dependent Chinese restaurant processes for image segmentation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation
        Interest point and salient region detections
        Video segmentation
  2. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Guide Proceedings

NIPS'11: Proceedings of the 25th International Conference on Neural Information Processing Systems

December 2011

2752 pages

ISBN:9781618395993

Publisher

Curran Associates Inc.

Red Hook, NY, United States

Publication History

Published: 12 December 2011

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Cited By

Nakano MFujiwara YKimura AYamada TUeda NRanzato MBeygelzimer ADauphin YLiang PVaughan J(2021)Permuton-induced Chinese restaurant processProceedings of the 35th International Conference on Neural Information Processing Systems10.5555/3540261.3542382(27695-27708)Online publication date: 6-Dec-2021
https://dl.acm.org/doi/10.5555/3540261.3542382
Zhang BZhang LGuo TWang YChen FGuo YFarooq F(2018)Simultaneous Urban Region Function Discovery and Popularity Estimation via an Infinite Urbanization Process ModelProceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3219819.3219987(2692-2700)Online publication date: 19-Jul-2018
https://dl.acm.org/doi/10.1145/3219819.3219987
Mitra A(2017)Bayesian approach to Spatio-temporally Consistent Simulation of Daily Monsoon Rainfall over IndiaProceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems10.1145/3139958.3139975(1-4)Online publication date: 7-Nov-2017
https://dl.acm.org/doi/10.1145/3139958.3139975
Hassanein AHussein MGomaa W(2016)Semantic analysis of crowded scenes based on non-parametric tracklet clusteringProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence10.5555/3061053.3061095(3389-3395)Online publication date: 9-Jul-2016
https://dl.acm.org/doi/10.5555/3061053.3061095
Song JHuang YQi XLi YLi FFu KHuang T(2016)Discovering hierarchical topic evolution in time-stamped documentsJournal of the Association for Information Science and Technology10.1002/asi.2343967:4(915-927)Online publication date: 1-Apr-2016
https://dl.acm.org/doi/10.1002/asi.23439
Ghosh SRaptis MSigal LSudderth E(2014)Nonparametric clustering with distance dependent hierarchiesProceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence10.5555/3020751.3020779(260-269)Online publication date: 23-Jul-2014
https://dl.acm.org/doi/10.5555/3020751.3020779
Ghosh SSudderth ELoper MBlack M(2012)From deformations to partsProceedings of the 26th International Conference on Neural Information Processing Systems - Volume 210.5555/2999325.2999358(1997-2005)Online publication date: 3-Dec-2012
https://dl.acm.org/doi/10.5555/2999325.2999358

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