Bag-of-Visual-Words for Cattle Identification from Muzzle Print Images
Cattle, buffalo and cow identification plays an influential role in cattle traceability from birth to slaughter, understanding disease trajectories and large-scale cattle ownership management. Muzzle print images are considered discriminating cattle biometric identifiers for biometric-based cattle i...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-11-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/9/22/4914 |
| _version_ | 1818296948121141248 |
|---|---|
| author | Ali Ismail Awad M. Hassaballah |
| author_facet | Ali Ismail Awad M. Hassaballah |
| author_sort | Ali Ismail Awad |
| collection | DOAJ |
| description | Cattle, buffalo and cow identification plays an influential role in cattle traceability from birth to slaughter, understanding disease trajectories and large-scale cattle ownership management. Muzzle print images are considered discriminating cattle biometric identifiers for biometric-based cattle identification and traceability. This paper presents an exploration of the performance of the bag-of-visual-words (BoVW) approach in cattle identification using local invariant features extracted from a database of muzzle print images. Two local invariant feature detectors—namely, speeded-up robust features (SURF) and maximally stable extremal regions (MSER)—are used as feature extraction engines in the BoVW model. The performance evaluation criteria include several factors, namely, the identification accuracy, processing time and the number of features. The experimental work measures the performance of the BoVW model under a variable number of input muzzle print images in the training, validation, and testing phases. The identification accuracy values when utilizing the SURF feature detector and descriptor were 75%, 83%, 91%, and 93% for when 30%, 45%, 60%, and 75% of the database was used in the training phase, respectively. However, using MSER as a points-of-interest detector combined with the SURF descriptor achieved accuracies of 52%, 60%, 67%, and 67%, respectively, when applying the same training sizes. The research findings have proven the feasibility of deploying the BoVW paradigm in cattle identification using local invariant features extracted from muzzle print images. |
| first_indexed | 2024-12-13T04:11:39Z |
| format | Article |
| id | doaj.art-39735a7b936d4475b04b77dbb8ba1539 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-13T04:11:39Z |
| publishDate | 2019-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-39735a7b936d4475b04b77dbb8ba15392022-12-22T00:00:03ZengMDPI AGApplied Sciences2076-34172019-11-01922491410.3390/app9224914app9224914Bag-of-Visual-Words for Cattle Identification from Muzzle Print ImagesAli Ismail Awad0M. Hassaballah1Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 971 87 Luleå, SwedenDepartment of Computer Science, Faculty of Computers and Information, South Valley University, P.O. Box 83523 Qena, EgyptCattle, buffalo and cow identification plays an influential role in cattle traceability from birth to slaughter, understanding disease trajectories and large-scale cattle ownership management. Muzzle print images are considered discriminating cattle biometric identifiers for biometric-based cattle identification and traceability. This paper presents an exploration of the performance of the bag-of-visual-words (BoVW) approach in cattle identification using local invariant features extracted from a database of muzzle print images. Two local invariant feature detectors—namely, speeded-up robust features (SURF) and maximally stable extremal regions (MSER)—are used as feature extraction engines in the BoVW model. The performance evaluation criteria include several factors, namely, the identification accuracy, processing time and the number of features. The experimental work measures the performance of the BoVW model under a variable number of input muzzle print images in the training, validation, and testing phases. The identification accuracy values when utilizing the SURF feature detector and descriptor were 75%, 83%, 91%, and 93% for when 30%, 45%, 60%, and 75% of the database was used in the training phase, respectively. However, using MSER as a points-of-interest detector combined with the SURF descriptor achieved accuracies of 52%, 60%, 67%, and 67%, respectively, when applying the same training sizes. The research findings have proven the feasibility of deploying the BoVW paradigm in cattle identification using local invariant features extracted from muzzle print images.https://www.mdpi.com/2076-3417/9/22/4914computer visionbiometricscattle identificationbag-of-visual-wordsmuzzle print images |
| spellingShingle | Ali Ismail Awad M. Hassaballah Bag-of-Visual-Words for Cattle Identification from Muzzle Print Images Applied Sciences computer vision biometrics cattle identification bag-of-visual-words muzzle print images |
| title | Bag-of-Visual-Words for Cattle Identification from Muzzle Print Images |
| title_full | Bag-of-Visual-Words for Cattle Identification from Muzzle Print Images |
| title_fullStr | Bag-of-Visual-Words for Cattle Identification from Muzzle Print Images |
| title_full_unstemmed | Bag-of-Visual-Words for Cattle Identification from Muzzle Print Images |
| title_short | Bag-of-Visual-Words for Cattle Identification from Muzzle Print Images |
| title_sort | bag of visual words for cattle identification from muzzle print images |
| topic | computer vision biometrics cattle identification bag-of-visual-words muzzle print images |
| url | https://www.mdpi.com/2076-3417/9/22/4914 |
| work_keys_str_mv | AT aliismailawad bagofvisualwordsforcattleidentificationfrommuzzleprintimages AT mhassaballah bagofvisualwordsforcattleidentificationfrommuzzleprintimages |