Year: 2025
Type: article
Abstract: This study aims to increase the accuracy of pixel-level detection of food regions extraction algorithms based on saliency detection, which predicts noticeable image areas that are attractive to the human eye. A method is proposed that refines food regions, detected from the noticeable areas, based on their characteristics using a graph-theory-based technique. The detected regions are typically based on noticeable portions of food items and their visually similar surroundings. In addition, state-of-the-art saliency detection is applied using a deep neural network, TransalNet, which has transformer layers. An experiment using 209 food images was conducted to demonstrate that the proposed TransalNet-based method significantly increased accuracy, which was measured by F-measure, by 3.38% compared with the conventional method. Additionally, the effectiveness of the proposed TransalNet-based method was extensively confirmed using images of both single and multiple food items.
Author: Takuya Futagami
Institution: Aichi Gakuin University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $2,306
OpenAlex ID: W4413224729