# From images in the wild to video-informed image classification

arXiv: https://arxiv.org/abs/2109.12040 · HTML: https://mlsanity.com/paper/2109.12040/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 24 Sep 2021.

## Builds on (most cited first, 6 of 6)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [The role of artificial intelligence in achieving the Sustainable Development Goals](https://mlsanity.com/paper/1905.00501/) (2019)
- [A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagery](https://mlsanity.com/paper/2010.08168/) (2020)
- [Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection](https://mlsanity.com/paper/1912.03538/) (2019)
- [Improving Image Classification with Location Context](https://mlsanity.com/paper/1505.03873/) (2015)
- [Machine Learning in Ethnobotany -- a first experiment](https://mlsanity.com/paper/2008.01611/) (2020)

## Abstract (arXiv)

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.