# Bag-of-Visual-Words for Cattle Identification from Muzzle Print Images
## 5 Discussion  
Traditional cattle identification methods such as ear tagging, branding and tattooing are vulnerable to losses, damages and fading. Electronic identification systems such as RFID-based systems involve many security and privacy challenges. Therefore, mapping biometric identifiers for animal identification has emerged as a hot research trend. Biometric-based cattle identification solves several problems in the conventional cattle identification methods [1; 51; 52]. Central to this study is the evaluation of the performance of the bag-of-visual-words (BoVW) model in cattle, buffalo and cow identification using muzzle print images. The study aims to investigate the feasibility of deploying the BoVW technique for a new biometric-based cattle identification system. To this end, two feature detection and description methods--namely, SURF and MSER--were used as feature extraction engines at the heart of the BoVW technique. The study offers two feature extraction scenarios. Initially, SURF is used as a local feature detector and descriptor. The second scenario considers MSER for point-of-interest detection, while SURF is used for feature description. The proposed BoVW-based system was evaluated using a muzzle print database of 7 images per 15 \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Training (\%)**} & \multicolumn{2}{c}{**Configuration Parameters**} & \multicolumn{2}{c}{**BoVW Performance Metrics**} \\ \cline{2-5}  & **No. of Images** & **No. of Features (Average)** & **Accuracy (\%)** & **Time (s)** \\ \hline
30\% & 30 & 208 & 52 & 15.0 \\
45\% & 45 & 197 & 60 & 19.7 \\
60\% & 60 & 191 & 67 & 22.1 \\
75\% & 75 & 192 & 67 & 28.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of the BoVW using maximally stable extremal regions (MSER) for points-of-interest detection and the SURF detector for feature description at every detected point. The table represents the number of images used in the training process, while the rest of the database is used for evaluation. The processing time is measured in seconds. \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **No. of Images** & **No. of Cattle** & **Total Accuracy (\%)** \\ \hline [PERSON] et al. [19] & 86 & 30 & 66.6 \\ [PERSON] et al. [22] & 105 & 15 & 93.3 \\ [PERSON] \& [PERSON] [23] & 120 & 08 & 90.0 \\ Proposed BoVW (SURF) & 105 & 15 & 93.0 \\ Proposed BoVW (MSER) & 150 & 15 & 67.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the obtained identification accuracies using the BoVW with SURF and MSER against other state-of-the-art methods. cattle heads. The evaluation database includes 105 images in total and the database was divided into training and evaluation subsets. The common issue of using SURF for feature detection and MSER is the identification accuracy value. The maximum achieved identification accuracy of using SURF is 93%, which is promising compared to other published methods. Moreover, using MSER achieved drastically low identification accuracy, which is incomparable to the accuracy achieved by using SURF. The 7% error in accuracy resulted from the similarity between muzzle print images because it is difficult to create distinguishing vocabularies for each image. Since this study is the first attempt to use the BoVW approach in cattle identification, it was hard to find similar comparison methods. Therefore, the comparison was performed with methods that extract local features from cattle muzzle print images. Although the BoVW approach has achieved reasonable accuracy using the SURF feature detector and descriptor, the achieved results are limited to the small-sized database of 105 images. Having a standard muzzle print image database and benchmarks for identification accuracy are still missing in the cattle identification domain [1]. The empirical work performed in this study has proven the possibility of utilizing BoVW in cattle identification; however, the BoVW feature extraction method should receive more considerations.