Evolutionary Computational Methods for Optimizing the Classification of Sea Stars in Underwater Images
Abstract
Using video and imagery for assessing the distribution and abundance of marine organisms is a valuable sampling method in that it is non-invasive and permits large volumes of data to be acquired. Quickly and accurately processing large volumes of imagery is a challenge for human analysts, which motivates the need for automated processing methods. In this paper, we present a method for the automatic classification of sea stars in underwater images. The method uses a very small number of features and is efficient. The classification process is optimized by using evolutionary computational methods. Experimental results show excellent performance of our proposed optimized classification approach.
Cite
Text
Mendes et al. "Evolutionary Computational Methods for Optimizing the Classification of Sea Stars in Underwater Images." IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, 2015. doi:10.1109/WACVW.2015.9Markdown
[Mendes et al. "Evolutionary Computational Methods for Optimizing the Classification of Sea Stars in Underwater Images." IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, 2015.](https://mlanthology.org/wacvw/2015/mendes2015wacvw-evolutionary/) doi:10.1109/WACVW.2015.9BibTeX
@inproceedings{mendes2015wacvw-evolutionary,
title = {{Evolutionary Computational Methods for Optimizing the Classification of Sea Stars in Underwater Images}},
author = {Mendes, André and Hoeberechts, Maia and Albu, Alexandra Branzan},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision Workshops},
year = {2015},
pages = {44-50},
doi = {10.1109/WACVW.2015.9},
url = {https://mlanthology.org/wacvw/2015/mendes2015wacvw-evolutionary/}
}