Name
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3390/s20174668 | SENSORS |
Keywords | DocType | Volume |
cognitive relevance,deep learning,crowd-sourcing,target user population,categorization,classification | Journal | 20 |
Issue | ISSN | Citations |
17 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gregor Koporec | 1 | 0 | 0.34 |
| Andrej Košir | 2 | 0 | 0.34 |
| Ales Leonardis | 3 | 1636 | 147.33 |
| Janez Pers | 4 | 265 | 19.24 |