Improving the Representation of CNN Based Features by Autoencoder for a Task of Construction Material Image Classification

Deep learning based model named Convolution Neural Network (CNN) has been extensively employed by diversified applications concerned images or videos data. Because training a specific CNN model for an application task consumes enormous machine resources and need many of the training data, consequently pre-trained models of CNN have been broadly used as the transfer-learning scenario. By the scenario, features had been learned from a pre-trained model by one source task can be proficiently sent further to another specific task in a concept of knowledge transferring. As a result, a task specific can be directly employed such pre-trained features or further train more by setting the pre-trained features as a starting point. Thereby, it takes not much time and can improve the performance from many referenced works. In this work, with a task specific on construction material images classification, we investigate on the transfer learning of GoogleNet and ResNet101 that pre-trained on ImageNet dataset (source task). By applying both of the transfer-learning schemes, they reveal quite satisfied results. The best for GoogleNet, it gets 95.50 percent of the classification accuracy by fine-tuning scheme. Where, for ResNet101, the best is of 95.00 percent by using fixed feature extractor scheme. Nevertheless, after the learning based representation methods are further employed on top of the transferred features, they expose more appeal results. By Autoencoder based representation method reveals the performance can improve more than PCA (Principal Component Analysis) in all cases. Especially, when the fixed feature extractor of ResNet101 is used as the input to Autoencoder, the classified result can be improved up to 97.83%. It can be inferred, just applying Autoencoder on top of the pre-trained transferred features, the performance can be improved by we have no need to fine-tune the complex pre-trained model.