Cognitive Relevance Transform for Population Re-Targeting

• Published in: Sensors (Basel, Switzerland) Switzerland), 2020-08, Vol.20 (17), p.4668
• Main Authors: Koporec, Gregor, Košir, Andrej, Leonardis, Aleš, Perš, Janez
• Format: Article
• Language: English
• Subjects: Algorithms; Animal cognition; Bias; categorization; Classification; Cognition; cognitive relevance; crowd-sourcing; Datasets; Deep learning; Human performance; Humans; Labels; target user population; Visual Perception
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20174668

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.