Deep Regression Neural Networks for Proportion Judgment

Mario Milicevic, Vedran Batos, Adriana Lipovac and Zeljka Car
Additional contact information
Mario Milicevic: Department of Electrical Engineering and Computing, University of Dubrovnik, 20000 Dubrovnik, Croatia
Vedran Batos: Department of Electrical Engineering and Computing, University of Dubrovnik, 20000 Dubrovnik, Croatia
Adriana Lipovac: Department of Electrical Engineering and Computing, University of Dubrovnik, 20000 Dubrovnik, Croatia
Zeljka Car: Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia

Future Internet, 2022, vol. 14, issue 4, 1-16

Abstract: Deep regression models are widely employed to solve computer vision tasks, such as human age or pose estimation, crowd counting, object detection, etc. Another possible area of application, which to our knowledge has not been systematically explored so far, is proportion judgment. As a prerequisite for successful decision making, individuals often have to use proportion judgment strategies, with which they estimate the magnitude of one stimulus relative to another (larger) stimulus. This makes this estimation problem interesting for the application of machine learning techniques. In regard to this, we proposed various deep regression architectures, which we tested on three original datasets of very different origin and composition. This is a novel approach, as the assumption is that the model can learn the concept of proportion without explicitly counting individual objects. With comprehensive experiments, we have demonstrated the effectiveness of the proposed models which can predict proportions on real-life datasets more reliably than human experts, considering the coefficient of determination (>0.95) and the amount of errors ( MAE < 2, RMSE < 3). If there is no significant number of errors in determining the ground truth, with an appropriate size of the learning dataset, an additional reduction of MAE to 0.14 can be achieved. The used datasets will be publicly available to serve as reference data sources in similar projects.

Keywords: deep learning; deep regression; computer vision; convolutional neural networks; proportion judgment (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/14/4/100/pdf (application/pdf)
https://www.mdpi.com/1999-5903/14/4/100/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:14:y:2022:i:4:p:100-:d:777449

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().