@inproceedings{a936afd4dc964c6e8db36af1b37f7442,
title = "From images in the wild to video-informed image classification",
abstract = "Image classifiers work effectively when applied on structured images, yet they often fail when applied on images with very high visual complexity. This paper describes experiments applying state-of-the-art object classifiers toward a unique set of 'images in the wild' with high visual complexity collected on the island of Bali. The text describes differences between actual images in the wild and images from Imagenet, and then discusses a novel approach combining informational cues particular to video with an ensemble of imperfect classifiers in order to improve classification results on video sourced images of plants in the wild.",
keywords = "Artificial intelligence in environmental studies, Classification, Images in the wild, Neural network-based image classification, Photography, Video, Video structure",
author = "Marc Bohlen and Raunaq Jain and Wawan Sujarwo and Varun Chandola",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021 ; Conference date: 13-12-2021 Through 16-12-2021",
year = "2021",
doi = "10.1109/ICMLA52953.2021.00109",
language = "English",
series = "Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "656--661",
editor = "Wani, \{M. Arif\} and Sethi, \{Ishwar K.\} and Weisong Shi and Guangzhi Qu and Raicu, \{Daniela Stan\} and Ruoming Jin",
booktitle = "Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021",
address = "United States",
}