Animal reidentification using restricted set classification

Kuncheva, Ludmila I.

doi:10.1016/j.ecoinf.2021.101225

Cited by 1 publication

(1 citation statement)

References 35 publications

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“…The challenge is to develop models that would work well in images with poor illumination or uncertain environments [19]. Concerning the studies using deep learning, individual reidentification has been critical to research on animal behavior in the wild [20]. Still, this alone cannot capture the full complexity of animals and environments.…”

Section: Data-driven Detection Of Wild Animalsmentioning

confidence: 99%

An Adaptive Embedding Network with Spatial Constraints for the Use of Few-Shot Learning in Endangered-Animal Detection

Feng

2022

IJGI

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Image recording is now ubiquitous in the fields of endangered-animal conservation and GIS. However, endangered animals are rarely seen, and, thus, only a few samples of images of them are available. In particular, the study of endangered-animal detection has a vital spatial component. We propose an adaptive, few-shot learning approach to endangered-animal detection through data augmentation by applying constraints on the mixture of foreground and background images based on species distributions. First, the pre-trained, salient network U2-Net segments the foregrounds and backgrounds of images of endangered animals. Then, the pre-trained image completion network CR-Fill is used to repair the incomplete environment. Furthermore, our approach identifies a foreground–background mixture of different images to produce multiple new image examples, using the relation network to permit a more realistic mixture of foreground and background images. It does not require further supervision, and it is easy to embed into existing networks, which learn to compensate for the uncertainties and nonstationarities of few-shot learning. Our experimental results are in excellent agreement with theoretical predictions by different evaluation metrics, and they unveil the future potential of video surveillance to address endangered-animal detection in studies of their behavior and conservation.

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Section: Data-driven Detection Of Wild Animalsmentioning

confidence: 99%

An Adaptive Embedding Network with Spatial Constraints for the Use of Few-Shot Learning in Endangered-Animal Detection

Feng

2022

IJGI

View full text Add to dashboard Cite

show abstract

Animal reidentification using restricted set classification

Cited by 1 publication

References 35 publications

An Adaptive Embedding Network with Spatial Constraints for the Use of Few-Shot Learning in Endangered-Animal Detection

An Adaptive Embedding Network with Spatial Constraints for the Use of Few-Shot Learning in Endangered-Animal Detection

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