Conformal anomaly detection for visual reconstruction using gestalt principles

Ilia Nouretdinov, Alexander Balinsky, Alexander Gammerman
Proceedings of the Ninth Symposium on Conformal and Probabilistic Prediction and Applications, PMLR 128:151-170, 2020.

Abstract

In this paper, we combine a modern machine learning technique called conformal predictors (CP) with elements of gestalt detection and apply them to the problem of visual perception in digital images. Our main task is to quantify several gestalt principles of visual reconstruction. We interpret an image/shape as being perceivable (meaningful) if it sufficiently deviates from randomness - in other words, the image could hardly happen by chance. These deviations from randomness are measured by using conformal prediction technique that can guarantee the validity under certain assumptions. The technique describes the detection of perceivable images that allows to bound the number of false alarms, i.e. the proportion of non-perceivable images wrongly detected as perceivable.

Cite this Paper

BibTeX
@InProceedings{pmlr-v128-nouretdinov20a, title = {Conformal anomaly detection for visual reconstruction using gestalt principles}, author = {Nouretdinov, Ilia and Balinsky, Alexander and Gammerman, Alexander}, booktitle = {Proceedings of the Ninth Symposium on Conformal and Probabilistic Prediction and Applications}, pages = {151--170}, year = {2020}, editor = {Gammerman, Alexander and Vovk, Vladimir and Luo, Zhiyuan and Smirnov, Evgueni and Cherubin, Giovanni}, volume = {128}, series = {Proceedings of Machine Learning Research}, month = {09--11 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v128/nouretdinov20a/nouretdinov20a.pdf}, url = {https://proceedings.mlr.press/v128/nouretdinov20a.html}, abstract = {In this paper, we combine a modern machine learning technique called conformal predictors (CP) with elements of gestalt detection and apply them to the problem of visual perception in digital images. Our main task is to quantify several gestalt principles of visual reconstruction. We interpret an image/shape as being perceivable (meaningful) if it sufficiently deviates from randomness - in other words, the image could hardly happen by chance. These deviations from randomness are measured by using conformal prediction technique that can guarantee the validity under certain assumptions. The technique describes the detection of perceivable images that allows to bound the number of false alarms, i.e. the proportion of non-perceivable images wrongly detected as perceivable. } }
Endnote
%0 Conference Paper %T Conformal anomaly detection for visual reconstruction using gestalt principles %A Ilia Nouretdinov %A Alexander Balinsky %A Alexander Gammerman %B Proceedings of the Ninth Symposium on Conformal and Probabilistic Prediction and Applications %C Proceedings of Machine Learning Research %D 2020 %E Alexander Gammerman %E Vladimir Vovk %E Zhiyuan Luo %E Evgueni Smirnov %E Giovanni Cherubin %F pmlr-v128-nouretdinov20a %I PMLR %P 151--170 %U https://proceedings.mlr.press/v128/nouretdinov20a.html %V 128 %X In this paper, we combine a modern machine learning technique called conformal predictors (CP) with elements of gestalt detection and apply them to the problem of visual perception in digital images. Our main task is to quantify several gestalt principles of visual reconstruction. We interpret an image/shape as being perceivable (meaningful) if it sufficiently deviates from randomness - in other words, the image could hardly happen by chance. These deviations from randomness are measured by using conformal prediction technique that can guarantee the validity under certain assumptions. The technique describes the detection of perceivable images that allows to bound the number of false alarms, i.e. the proportion of non-perceivable images wrongly detected as perceivable.
APA
Nouretdinov, I., Balinsky, A. & Gammerman, A.. (2020). Conformal anomaly detection for visual reconstruction using gestalt principles. Proceedings of the Ninth Symposium on Conformal and Probabilistic Prediction and Applications, in Proceedings of Machine Learning Research 128:151-170 Available from https://proceedings.mlr.press/v128/nouretdinov20a.html.

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