Biometric animal databases from field photographs: identification of individual zebra in the wild

Authors:
Mayank Lahiri;Chayant Tantipathananandh;Rosemary Warungu;Daniel I. Rubenstein;Tanya Y. Berger-Wolf
Affiliations:
University of Illinois at Chicago;University of Illinois at Chicago;The Ol'Pejeta Conservancy, Laikipia, Kenya;Princeton University;University of Illinois at Chicago
Venue:
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Year:
2011

Citing 11
Cited 1

The String-to-String Correction Problem

Journal of the ACM (JACM)
Shock Graphs and Shape Matching

International Journal of Computer Vision
Computation of Normalized Edit Distance and Applications

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Computation of Normalized Edit Distances

IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts

IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching and Retrieval of Distorted and Occluded Shapes Using Dynamic Programming

IEEE Transactions on Pattern Analysis and Machine Intelligence
The TREC question answering track

Natural Language Engineering
Hardware design experiences in ZebraNet

SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
A Semi-automatic Approach to Photo Identification of Wild Elephants

IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
An affine invariant curve matching method for photo-identification of marine mammals

Pattern Recognition
Combined morphological-spectral unsupervised image segmentation

IEEE Transactions on Image Processing

Identification of great apes using gabor features and locality preserving projections

Proceedings of the 1st ACM international workshop on Multimedia analysis for ecological data

Quantified Score

Hi-index	0.00

Visualization

Abstract

We describe an algorithmic and experimental approach to a fundamental problem in field ecology: computer-assisted individual animal identification. We use a database of noisy photographs taken in the wild to build a biometric database of individual animals differentiated by their coat markings. A new image of an unknown animal can then be queried by its coat markings against the database to determine if the animal has been observed and identified before. Our algorithm, called StripeCodes, efficiently extracts simple image features and uses a dynamic programming algorithm to compare images. We test its accuracy against two different classes of methods: Eigenface, which is based on algebraic techniques, and matching multi-scale histograms of differential image features, an approach from signal processing. StripeCodes performs better than all competing methods for our dataset, and scales well with database size.