Stochastic Segmentation Trees for Multiple Ground Truths

Jake Snell, Richard S. Zemel
Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, PMLR R15:750-759, 2017.

Abstract

A strong machine learning system should aim to produce the full range of valid interpreta- tions rather than a single mode in tasks involv- ing inherent ambiguity. This is particularly true for image segmentation, in which there are many sensible ways to partition an image into regions. We formulate a tree-structured probabilistic model, the stochastic segmenta- tion tree, that represents a distribution over segmentations of a given image. We train this model by optimizing a novel objective that quantifies the degree of match between statis- tics of the model and ground truth segmen- tations. Our method allows learning of both the parameters in the tree and the structure itself. We demonstrate on two datasets, in- cluding the challenging Berkeley Segmenta- tion Dataset, that our model is able to success- fully capture the range of ground truths and to produce novel plausible segmentations beyond those found in the data.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR15-snell17a, title = {Stochastic Segmentation Trees for Multiple Ground Truths}, author = {Snell, Jake and Zemel, Richard S.}, booktitle = {Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence}, pages = {750--759}, year = {2017}, editor = {Elidan, Gal and Kersting, Kristian}, volume = {R15}, series = {Proceedings of Machine Learning Research}, month = {11--15 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r15/main/assets/snell17a/snell17a.pdf}, url = {https://proceedings.mlr.press/r15/snell17a.html}, abstract = {A strong machine learning system should aim to produce the full range of valid interpreta- tions rather than a single mode in tasks involv- ing inherent ambiguity. This is particularly true for image segmentation, in which there are many sensible ways to partition an image into regions. We formulate a tree-structured probabilistic model, the stochastic segmenta- tion tree, that represents a distribution over segmentations of a given image. We train this model by optimizing a novel objective that quantifies the degree of match between statis- tics of the model and ground truth segmen- tations. Our method allows learning of both the parameters in the tree and the structure itself. We demonstrate on two datasets, in- cluding the challenging Berkeley Segmenta- tion Dataset, that our model is able to success- fully capture the range of ground truths and to produce novel plausible segmentations beyond those found in the data.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Stochastic Segmentation Trees for Multiple Ground Truths %A Jake Snell %A Richard S. Zemel %B Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2017 %E Gal Elidan %E Kristian Kersting %F pmlr-vR15-snell17a %I PMLR %P 750--759 %U https://proceedings.mlr.press/r15/snell17a.html %V R15 %X A strong machine learning system should aim to produce the full range of valid interpreta- tions rather than a single mode in tasks involv- ing inherent ambiguity. This is particularly true for image segmentation, in which there are many sensible ways to partition an image into regions. We formulate a tree-structured probabilistic model, the stochastic segmenta- tion tree, that represents a distribution over segmentations of a given image. We train this model by optimizing a novel objective that quantifies the degree of match between statis- tics of the model and ground truth segmen- tations. Our method allows learning of both the parameters in the tree and the structure itself. We demonstrate on two datasets, in- cluding the challenging Berkeley Segmenta- tion Dataset, that our model is able to success- fully capture the range of ground truths and to produce novel plausible segmentations beyond those found in the data. %Z Reissued by PMLR on 04 October 2026.
APA
Snell, J. & Zemel, R.S.. (2017). Stochastic Segmentation Trees for Multiple Ground Truths. Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R15:750-759 Available from https://proceedings.mlr.press/r15/snell17a.html. Reissued by PMLR on 04 October 2026.

Related Material