# Entropy-Based Ensemble of Convolutional Neural Networks for Clothes Texture Pattern Recognition
## 5 Discussion  
The end-to-end deep learning technique used in this work proves its effectiveness in capturing discriminative features outperforming the hand-engineered features. As shown in Table 4, the entropy-based ensemble learner shows a significant improvement compared with most related works that rely on a single clothes attribute: its pattern. It shows its robustness as it generalizes well and gives good performance when used on two different datasets.  
The experimental results show the effectiveness of the fused learning algorithms in comparison with using each one separately. This improvement in performance definitely requires that each individual classifier is performing well. Incorporating different blocks with the pre-trained model (ResNet50) as a participating model resulted in diverse models, each of which extracts pattern features from different angles, yielding more reliable complementary prediction. Analyzing each pattern (label) classification performance in each individual model in Figure 11, it can be deduced that the classifiers are diverse in a way that each classifier tackles the data differently as they perform differently in some  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Ref** & **Method** & **Accuracy** \\ \hline \multirow{3}{*}{CCYN [16]} & [PERSON] et al. [16] & (SIFT+STA+RanonSig) + SVM & 92.55 \% \\  & [PERSON] [17] & (SURF+GLCM+DWT) + SVM & 76.25\% \\  & CNN–Ensemble learner & 96.03\% \\ \hline \multirow{2}{*}{Collected from Google [19]} & [PERSON] et al. [19] & CNN (ResNet101)–Transfer learning & 91.7\% \\  & & CNN–Ensemble learner & 98.18\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison with state-of-the-art works.  
Figure 10: Confusion matrix showing the decision making of the ensemble classifier.  
patterns. For example, \"solid\"-labeled clothes are classified much better in the model that incorporates the non-local (NL) block than the one that incorporate coordinate attention (CA). This could be because CA embedded positional information which can be confused for \"solid\"-labeled clothes since, even they are solid, the type of the fabric itself may affect the prediction and be considered as checkered or dotted, such as in some kinds of fabrics like knitted wool or linen. Furthermore, it can be shown that ResNet50 incorporated with the CA block performs the best among the other models in capturing \"stripes\" and \"zigzag\" patterns, which span the whole shot by their nature.  
As per any deep learning method, the crucial consideration for success is the volume and diversity of the training dataset. This work can be improved using a more efficient approach to augment training data, such as active learning, used in [28], where the learner can determine what the instances will be trained on according to some 'worthiness' indicator. Moreover, a massive clothes dataset with comprehensive annotations used for multi-attribute clothes recognition such as DeepFashion [8] can be used to train the proposed system. However, in this work, it is only tested against available single-attribute works' datasets.