Conformal anomaly detection for visual reconstruction using gestalt principles

Nouretdinov, Ilia, Balinsky, Alexander ORCID: https://orcid.org/0000-0002-8151-4462 and Gammerman, Alexander 2020. Conformal anomaly detection for visual reconstruction using gestalt principles. Presented at: 9th Symposium on Conformal and Probabilistic Prediction with Applications (COPA 2020), Verona, Italy, 9-11 September 2020. Proceedings of Machine Learning Research. , vol.128 pp. 1-20.

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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.

Item Type:	Conference or Workshop Item (Paper)
Status:	Published
Schools:	Mathematics
ISSN:	2640-3498
Date of First Compliant Deposit:	14 September 2020
Date of Acceptance:	3 July 2020
Last Modified:	07 Nov 2022 11:12
URI:	https://orca.cardiff.ac.uk/id/eprint/134826

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