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Article

Cognitive Relevance Transform for Population Re-Targeting

1
Gorenje, d. o. o., Partizanska cesta 12, SI-3320 Velenje, Slovenia
2
Faculty of Electrical Engineering, University of Ljubljana, Tržaška cesta 25, SI-1000 Ljubljana, Slovenia
3
School of Computer Science, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
*
Authors to whom correspondence should be addressed.
Sensors 2020, 20(17), 4668; https://doi.org/10.3390/s20174668
Received: 21 July 2020 / Revised: 13 August 2020 / Accepted: 14 August 2020 / Published: 19 August 2020
(This article belongs to the Section Intelligent Sensors)
This work examines the differences between a human and a machine in object recognition tasks. The machine is useful as much as the output classification labels are correct and match the dataset-provided labels. However, very often a discrepancy occurs because the dataset label is different than the one expected by a human. To correct this, the concept of the target user population is introduced. The paper presents a complete methodology for either adapting the output of a pre-trained, state-of-the-art object classification algorithm to the target population or inferring a proper, user-friendly categorization from the target population. The process is called ‘user population re-targeting’. The methodology includes a set of specially designed population tests, which provide crucial data about the categorization that the target population prefers. The transformation between the dataset-bound categorization and the new, population-specific categorization is called the ‘Cognitive Relevance Transform’. The results of the experiments on the well-known datasets have shown that the target population preferred such a transformed categorization by a large margin, that the performance of human observers is probably better than previously thought, and that the outcome of re-targeting may be difficult to predict without actual tests on the target population. View Full-Text
Keywords: cognitive relevance; deep learning; crowd-sourcing; target user population; categorization; classification cognitive relevance; deep learning; crowd-sourcing; target user population; categorization; classification
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MDPI and ACS Style

Koporec, G.; Košir, A.; Leonardis, A.; Perš, J. Cognitive Relevance Transform for Population Re-Targeting. Sensors 2020, 20, 4668. https://doi.org/10.3390/s20174668

AMA Style

Koporec G, Košir A, Leonardis A, Perš J. Cognitive Relevance Transform for Population Re-Targeting. Sensors. 2020; 20(17):4668. https://doi.org/10.3390/s20174668

Chicago/Turabian Style

Koporec, Gregor, Andrej Košir, Aleš Leonardis, and Janez Perš. 2020. "Cognitive Relevance Transform for Population Re-Targeting" Sensors 20, no. 17: 4668. https://doi.org/10.3390/s20174668

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