# Leaf Image Recognition Based on Bag of Features
## 5 Experiments and Analysis
### Comparison of Different Methods

Table 2: Detail information of the four datasets. recognition rate under the very superior experimental conditions. Overall, the proposed method is superior to most of other existing methods. Table 4 shows the results of different methods on the Swedish dataset. It contains 1125 sample images from 15 species, with 75 images per species. The authors of [49] proposed SMF, which utilizes the area ratio to quantify the convexity/concavity of each contour point at different scales to construct margin feature, and they used a combination of morphological features as shape feature. [PERSON] et al. [50] introduced a novel multiscale Fourier descriptor (MF) based on triangular features, which effectively captures the local and global features of leaf shape. MARCH [51] (multiscale arch height) is a novel multiscale shape description. [PERSON] et al. [52] proposed a hierarchical string cuts (HSCs) method. CSD [53] is a counting-based shape descriptor for leaf recognition, which can capture global and local shape information independently. CBOW is a shape recognition algorithm based on the curvature bag of words (CBOW) model. Generally, the recognition accuracy is improved with the increase of the number of training samples. When the training number of the method Z&H [11] is 750, the recognition result is significantly improved, which is slightly higher than the method we propose. In addition, compared with the other existing methods, the proposed method is superior. S-Inception [48] obtained the lowest recognition accuracy, while MEW [32], MF [50] and CSD [53] were close to the accuracy of the proposed method. The recognition accuracies of the other methods were also very close. For ICL dataset, some researchers only use part of samples from dataset. Hence, the detailed comparisons are listed in Table 5. GTCLC [55] is a leaf classification method using multiple descriptors. [PERSON] et al. proposed a new local binary pattern (LBP) descriptor, and they combined it with geometric, shape, texture, and color features for leaf recognition. The authors of [56] used several different descriptors to extract texture and shape features and proposed a pre-training method based on the PID to improve the DBNs. DWSRC (discriminant WSRC) [57] is the method proposed by [PERSON] et al. for large-scale plant species recognition. The authors of [58] presented the novel relative sub-image sparse coefficient (RSSC) algorithm for mobile devices. DBNs chose 50 species for training and testing and it obtained the highest accuracy with 96%, higher than the proposed method; however, \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **Species** & **Total Images** & **Training/Testing Images** & **Accuracy (\%)** \\ \hline
**SMF**[49] & 15 & 1125 & 375/750 & 95.82 \\
**Z&H**[11] & 15 & 1125 & 375/750 & 95.86 \\
**MF**[50] & 15 & 1125 & 375/750 & 97.60 \\
**MARCH**[51] & 15 & 1125 & \(-\)/\(-\) & 96.21 \\
**MLBP**[44] & 15 & 1125 & \(-\)/\(-\) & 96.83 \\
**HSCs**[52] & 15 & 1125 & 375/750 & 96.91 \\
**CSD**[53] & 15 & 1125 & \(-\)/\(-\) & 97.07 \\
**MEW**[32] & 15 & 1125 & 555/570 & 96.53 \\
**CBOW**[54] & 15 & 1125 & \(-\)/\(-\) & 97.23 \\
**S-Inception**[48] & n & \(-\) & 20/\(-\) & 91.37 \\
**Proposed Method** & 15 & 1125 & 555/570 & 97.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of proposed method with existing methods on Swedish dataset.