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Cognitive Relevance Transform for Population Re-Targeting.


ABSTRACT: 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.

SUBMITTER: Koporec G 

PROVIDER: S-EPMC7506759 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Cognitive Relevance Transform for Population Re-Targeting.

Koporec Gregor G   Košir Andrej A   Leonardis Aleš A   Perš Janez J  

Sensors (Basel, Switzerland) 20200819 17


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-t  ...[more]

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