150,203 Encounters · 19,459 Individuals
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Wildbook for Zebras applies computer vision algorithms and deep learning to identify and track individual zebras across hundreds of thousands of photos. We help researchers to collaborate with each other and citizen scientists to contribute to the effort. A.I. scales and speeds research and conservation.
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Algorithms
MIEW-ID
MIEW-ID (µID) is a modern deep learning algorithm used to identify individuals. MIEW-ID can learn what makes images similar or dissimilar (or what differentiates one animal from another) across a wide range of species. Distinguishing individuals through their unique body markings is a key concept for wildlife conservation. MIEW-ID learns embeddings for images from the database. Embeddings are the unique markings that represent individuals. When new images are analyzed, their embeddings are matched against clusters of those in the database. As an added benefit, MIEW-ID is able generate visualizations of matched features, providing import inspectability inside its neural network. Learn more about MIEW-ID.
HOTSPOTTER
Hotspotter is a SIFT-based computer vision algorithm. It analyzes the textures in an image to find distinct patterning, or "hot spots", and then compares those against other images in the database. Unlike machine learning-based approaches, HotSpotter can help build new catalogs for new species that need to match individuals but don’t have the training data yet for machine learning-based approaches. Hotspotter can also match new individuals without the need for network retraining. Hotspotter produces a ranked list of potential matches, increasing match scores with increasing similarity. Learn more about Hotspotter.
MIEW-ID (µID) is a modern deep learning algorithm used to identify individuals. MIEW-ID can learn what makes images similar or dissimilar (or what differentiates one animal from another) across a wide range of species. Distinguishing individuals through their unique body markings is a key concept for wildlife conservation. MIEW-ID learns embeddings for images from the database. Embeddings are the unique markings that represent individuals. When new images are analyzed, their embeddings are matched against clusters of those in the database. As an added benefit, MIEW-ID is able generate visualizations of matched features, providing import inspectability inside its neural network. Learn more about MIEW-ID.
HOTSPOTTER
Hotspotter is a SIFT-based computer vision algorithm. It analyzes the textures in an image to find distinct patterning, or "hot spots", and then compares those against other images in the database. Unlike machine learning-based approaches, HotSpotter can help build new catalogs for new species that need to match individuals but don’t have the training data yet for machine learning-based approaches. Hotspotter can also match new individuals without the need for network retraining. Hotspotter produces a ranked list of potential matches, increasing match scores with increasing similarity. Learn more about Hotspotter.
