# Deep Convolutional Neural Network with KNN Regression for Automatic Image Annotation
## 4 Experiments and Result Analysis
### Scenario 2: Comparing Our Method to the State of the Art
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **No. Cpt** & **P** & **R** & **F1** & **N+** \\ \hline CNN-R (2015) [7] & 374 & 32 & 41.3 & 36.1 & 166 \\ KCCA (2015) [7] & 374 & 39 & 53 & 44.9 & 184 \\ CCA-KNN (2015) [7] & 374 & 42 & 52 & 46.5 & 201 \\ Group Sparsity (2015) [47] & 260 & 30 & 33 & 31.4 & 146 \\ GLKNN (2015) [48] & 260 & 36 & 47 & 40.8 & 184 \\ MIAPS (2015) [49] & 260 & 39.98 & 42.66 & 41.28 & 177 \\ MVSAE (2015) [50] & 260 & 37 & 47 & 42 & 175 \\ IJNMF (2015) [51] & 260 & 35 & 43 & 39.1 & 175 \\ SLED (2015) [52] & 260 & 35 & 51 & 41.5 & - \\ AWD-IJNNN (2016) [53] & 260 & 42 & 55 & 47.7 & 198 \\ CNN-AT (2016) [54] & 374 & 26 & 17 & 21 & 88 \\ NSDDML (2016) [55] & 260 & 44.12 & 51.76 & 47.76 & 194 \\ MLDL (2016) [56] & 260 & 45 & 49 & 47 & 198 \\ LDMKL (2017) [57] & 200 & 29 & 44 & 35 & 179 \\ SDMKL (2017) [57] & 200 & 25 & 38 & & 158 \\ L-ADA (2017) [58] & 260 & 31 & 38 & 34 & 164 \\ NL-ADA (2017) [58] & 260 & 32 & 40 & 36 & 173 \\ MVG-NMF (2017) [59] & 260 & 44 & 47.5 & 45.6 & 197 \\ PRM (2017) [60] & 260 & 40.78 & 53.64 & 46.33 & 205 \\ VSE-2 PKNN-ML (2018) [61] & 260 & 41 & 52 & 46 & 205 \\ PRM DEEP (2018) [62] & 260 & 45.3 & 51.73 & 48.3 & 201 \\ CCAKNN (2018) [63] & 260 & 41 & 43 & 42 & 185 \\ IDFRW (2018) [64] & 260 & 38 & 49 & 43 & 185 \\ Holistic CDNI (2018) [65] & 260 & 29.8 & 32.1 & 30.9 & 162 \\ approach & OPSL (2018) [66] & 260 & 38.3 & 55 & 45.2 & \\ E2E-DCNN (2019) [67] & 260 & 41 & 55 & 47 & 192 \\ SEM (2019) [68] & 260 & 37 & 52 & 43 & - \\ L-Global CA (2019) [69] & 260 & 36 & 45 & & 189 \\ S-Global CA (2019) [69] & 260 & 36 & 46 & & 194 \\ L-Classwise CA (2019) [69] & 260 & 36 & 45 & & 192 \\ LL-PLSA (2020) [70] & 260 & 37 & 48 & 42 & - \\ RDPGKNN (2020) [71] & 260 & 40 & 45 & 40 & 195 \\ Weight-KNN (2020) [23] & 260 & 22 & 15 & 18 & - \\ [PERSON] et al. (2020) [72] & 260 & 55.46 & 56.55 & 56 & 212 \\ GCN (2020) [73] & 260 & 48 & 52 & 49 & 200 \\ CNN-THOP (2020) [74] & 260 & 52.7 & 58.3 & 55.3 & - \\ SSGL (2020) [75] & 260 & 34 & 47 & 40 & 190 \\ [PERSON] et al. (2020) [76] & 374 & 60 & 68 & 64 & 228 \\ PLSA-MB (2020) [77] & 260 & 26 & 30 & 27.9 & \\ TIAA (2020) [78] & 260 & 38.4 & 48.6 & 42.9 & 177 \\ [PERSON] et al. (2021) [79] & 260 & 26.93 & 41.43 & 32.64 & 161 \\ TSEM (2021) [80] & 260 & 38 & 46 & 42 & - \\ TSEM+LQP (2021) [80] & 260 & 45 & 40 & 43 & - \\ SSI-AWF (2021) [81] & 260 & 51 & 48 & 49.5 & 203 \\ CNN-SPP (2021) [81] & 260 & 46 & 43 & 44.4 & 196 \\ HMAA (2021) [1] & 260 & 43 & 54 & 48 & \\ MVRSC (2021) [82] & 260 & 54.3 & 42.9 & 47.9 & \\ LDA-ECC (2021) [81] & 260 & 35 & 36 & 35.5 & 148 \\ \hline MLSIA (2015) [19] & 374 & 23.35 & 26.24 & 23.54 & - \\ ANNOR-G (2015) [83] & 260 & 22 & 29 & 25 & 129 \\ [PERSON] et al. (2016) [16] & 374 & 57.61 & 53.04 & 53.85 & - \\ BG (2019) [84] & 374 & 33 & 41 & & 170 \\ approach & TG (2019) [84] & 374 & 36 & 45 & & 189 \\ [PERSON] et al. (2020) [85] & 260 & 28 & 96 & 43 & - \\
**Our method** & 374 & 48.63 & 64.94 & 54.85 & 236 \\
**Our method** & 260 & 59.45 & 65.01 & 58.89 & 212 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A comparison between our method and other recent related works in terms of precision (P), recall (R), F1, and N+. The involved works adopt one of the following scenarios: considering 260 concepts or considering 374 concepts, as shown in the ‘No. Cpt\({}^{\prime}\) column. From Table 2, it appears that our proposed segmentation-based AIA method outperforms the majority of the stated related works in both scenarios of 274 and 260 concepts. If we take as an instance the top two F1 scores yielded by the related works [PERSON] et al. (2020) [72] and CNN-THOP (2020) [74] in the scenario of 260 concepts, we can clearly see that the outcomes of our method exceed those of both methods by 5% at least. Furthermore, the F1 score obtained by our method is at least 10% higher than that obtained by other recent studies such as GCN (2020) [73], SSL-AWF (2021) [81], and MVRSC (2021) [82].