Version 1
: Received: 17 June 2022 / Approved: 27 June 2022 / Online: 27 June 2022 (08:56:21 CEST)
How to cite:
Pensiri, F.; Visutsak, P. Optimization of Hue, Brightness, Luminance, and Saturation Parameters for Video Segmentation Based on Evolutionary Algorithms. Preprints2022, 2022060356. https://doi.org/10.20944/preprints202206.0356.v1
Pensiri, F.; Visutsak, P. Optimization of Hue, Brightness, Luminance, and Saturation Parameters for Video Segmentation Based on Evolutionary Algorithms. Preprints 2022, 2022060356. https://doi.org/10.20944/preprints202206.0356.v1
Pensiri, F.; Visutsak, P. Optimization of Hue, Brightness, Luminance, and Saturation Parameters for Video Segmentation Based on Evolutionary Algorithms. Preprints2022, 2022060356. https://doi.org/10.20944/preprints202206.0356.v1
APA Style
Pensiri, F., & Visutsak, P. (2022). Optimization of Hue, Brightness, Luminance, and Saturation Parameters for Video Segmentation Based on Evolutionary Algorithms. Preprints. https://doi.org/10.20944/preprints202206.0356.v1
Chicago/Turabian Style
Pensiri, F. and Porawat Visutsak. 2022 "Optimization of Hue, Brightness, Luminance, and Saturation Parameters for Video Segmentation Based on Evolutionary Algorithms" Preprints. https://doi.org/10.20944/preprints202206.0356.v1
Abstract
Video segmentation is crucial in a variety of practical applications especially in computer visions. Most of recent works in video segmentation are focusing on Deep learning based video segmentation, there are rooms for improvement in respect of the evolutionary algorithms. This paper aims to propose the novel method to video segmentation by using the optimization of segmentation parameters based on ensemble-based random forest and gradient boosting decision tree. The experimental results show Pareto front of segmentation parameters (hue, brightness, luminance, and saturation). Our optimization model yields accuracy: 85% +/-8.85 % (micro average: 85.00 %), average class precision: 84.88%, and average class recall: 85%. We also show the video segmentation results based on our optimization method and compare our results with Kinect-based video segmentation.
Keywords
optimization; video segmentation; decision tree; random forest; gradient boost tree
Subject
Computer Science and Mathematics, Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.