Name
Abstract | ||
|---|---|---|
The quality of animal identification system plays an important role for producers to make management decisions about their herd or individual animals. The animal identification is also important to animal traceability systems to ensure the integrity of the food chain. Usually, recordings and readings of tags-based systems are used to identify an animal, but only effective in eradication programs of national disease. Recently, animal biometric-based solutions, e.g. muzzle imaging system, offer an effective and secure, and rapid method of addressing the requirements of animal identification and traceability systems. In this paper, we present an identification system based on muzzle images. The identification process is based on Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Tucker Tensor Decomposition. This selected classifiers we compared on the same dataset of muzzle images with different experiment settings. The results we evaluated by F-score. The best F-score result gives us the Tucker Tensor Decomposition. It achieved the median of F-score 0.750. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-29504-6_11 | PROCEEDINGS OF THE SECOND INTERNATIONAL AFRO-EUROPEAN CONFERENCE FOR INDUSTRIAL ADVANCEMENT (AECIA 2015) |
Keywords | DocType | Volume |
Animal identification,Recognition,Identification,Tucker decomposition,Tensor,Support Vector Machine,Singular Value Decomposition,Linear Discriminant Analysis | Conference | 427 |
ISSN | Citations | PageRank |
2194-5357 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lukás Zaorálek | 1 | 0 | 0.34 |
| Michal Prilepok | 2 | 32 | 6.45 |
| Václav Snasel | 3 | 1261 | 210.53 |