BIRDSNAP: An Electronic Field Guide to Birds
Birdsnap is a free electronic field guide covering 500 of the most common North American bird species, available as a web site or an iPhone app. Researchers from Columbia University and the University of Maryland developed Birdsnap using computer vision and machine learning to explore new ways of identifying bird species. Birdsnap automatically discovers visually similar species and makes visual suggestions for how they can be distinguished. In addition, Birdsnap uses visual recognition technology to allow users who upload bird images to search for visually similar species. Birdsnap estimates the likelihood of seeing each species at any location and time of year based on sightings records, and uses this likelihood both to produce a custom guide to local birds for each user and to improve the accuracy of visual recognition.

The genesis of Birdsnap (and its predecessors Leafnsap and Dogsnap) was the realization that many techniques used for face recognition developed by Peter Belhumeur (Columbia University) and David Jacobs (University of Maryland) could also be applied to automatic species identification. State-of-the-art face recognition algorithms rely on methods that find correspondences between comparable parts of different faces, so that, for example, a nose is compared to a nose, and an eye to an eye. In the same way, Birdsnap detects the parts of a bird, so that it can examine the visual similarity of comparable parts of the bird.
Our first electronic field guide Leafsnap, produced in collaboration with the Smithsonian Institution, was launched in May 2011. This free iPhone app uses visual recognition software to help identify tree species from photographs of their leaves. Leafsnap currently includes the trees of the northeastern US and will soon grow to include the trees of the United Kingdom. Leafsnap has been downloaded by over a million users, and discussed extensively in the press (see, for more information). In 2012, we launched Dogsnap, an iPhone app that allows you to use visual recognition to help determine dog breeds. Dogsnap contains images and textual descriptions of over 150 breeds of dogs recognized by the American Kennel Club.

For their inspiration and advice on bird identification, we thank the UCSD Computer Vision group, especially Serge Belongie, Catherine Wah, and Grant Van Horn; the Caltech Computational Vision group, especially Pietro Perona, Peter Welinder, and Steve Branson; the alumni of these groups Ryan Farrell (now at BYU), Florian Schroff (at Google), and Takeshi Mita (at Toshiba); and the Visipedia effort.

Columbia University

Seung Woo Lee built the web site.

Michelle Alexander built the first versions of the iPhone app.

Thomas Berg built the visual recognition system and manages the development effort.

Peter Belhumeur supervises the project.

Jiongxin Liu built the bird part localization system.

Chris D'Angelo is building the iPhone app.

Bohong Zhao added range maps to the iPhone app.

Enze Li is working on the iPhone app.

University of Maryland

David Jacobs is an advisor on the project.

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