Abstract
Cattle muzzle classification can be considered as a biometric identifier important to animal traceability systems to ensure the integrity of the food chain. This paper presents a muzzle-based classification system that combines local invariant features with graph matching. The proposed approach consists of three phases; namely feature extraction, graph matching, and matching refinement. The experimental results showed that our approach is superior than existing works as ours achieves an all correct identification for the tested images. In addition, the results proved that our proposed method achieved this high accuracy even if the testing images are rotated in various angles.
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Monteiro, F.C. (2016). Automatic Cattle Identification Using Graph Matching Based on Local Invariant Features. In: Campilho, A., Karray, F. (eds) Image Analysis and Recognition. ICIAR 2016. Lecture Notes in Computer Science(), vol 9730. Springer, Cham. https://doi.org/10.1007/978-3-319-41501-7_88
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DOI: https://doi.org/10.1007/978-3-319-41501-7_88
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