Name
Abstract | ||
|---|---|---|
Visual features associated with cattle and pigs cannot just be used for individual biometric identification, they can also be used to organize the domain into distinct breeds. In this paper, we use cropped muzzle images of pigs for breed identification. Gradient patch density maps are first created, and then the patch density profile distribution tailored to a specific breed is learnt to characterize the feature space for each of the four pig breeds: Duroc, Ghungroo, Hampshire, and Yorkshire. A Maximal Likelihood (ML) inferencing at the patch level followed by a second-tier decision fusion based on majority vote has been used to detect the breed from any query feature computed from an arbitrary muzzle image. Duroc, Ghungroo, and Yorkshire show good classification accuracies of 75.86%, 70.59%, and 100%, respectively, while the accuracy drops for Hampshire to 58.78% on account of the intrinsic white patch diversity in the breed and its similarity to Duroc and Ghungroo on some counts. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-981-32-9088-4_7 | PROCEEDINGS OF 3RD INTERNATIONAL CONFERENCE ON COMPUTER VISION AND IMAGE PROCESSING, CVIP 2018, VOL 1 |
Keywords | DocType | Volume |
Gradient significance map, Pig, Breed identification, Biometric, Maximal likelihood, Conditional density function, Decision fusion, Majority vote | Conference | 1022 |
ISSN | Citations | PageRank |
2194-5357 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shoubhik Chakraborty | 1 | 0 | 0.34 |
| Kannan Karthik | 2 | 0 | 0.68 |
| Santanu Banik | 3 | 0 | 0.68 |