From images in the wild to video-informed image classification
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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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 17 references that are in the map. First posted on arXiv 24 Sep 2021.
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References · 6
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
- 2019The role of artificial intelligence in achieving the Sustainable Development Goalscited by 100
- 2020A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagerycited by 87
- 2019Context R-CNN: Long Term Temporal Context for Per-Camera Object Detectioncited by 45
- 2015Improving Image Classification with Location Contextcited by 33
- 2020Machine Learning in Ethnobotany -- a first experimentcited by 2
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