Abstract：In view of the limited feature extraction capabilities of the Deep Continuous Clustering(DCC)algorithm,this paper proposes a new Recurrent Convolutional Auto-Encoder(R-CAE),which is used to solve the problem that complex images cannot extract sufficient and effective detailed features.The autoencoder combines the gated recurrent network and the convolutional network to construct the coding layer;at the same time,the spatial domain attention channel is added to the GRU part of the gated recurrent network to enhance the feature learning ability of the network.The decoding layer of the self-encoder adopts an asymmetric CNN structure.The image information is first encoded by the R-CAE autoencoder,and the detailed information is obtained and then passed into the classic convolutional neural network to learn features;by continuously optimizing the objective function of the clustering algorithm,when the optimization result is close to or reaches the set clustering threshold,the final clustering result is obtained to realize classification.In the training process,the model adopts the pre-training method,firstly determine the autoencoder parameters;then combine the coding part and the classical network learning and training to fine-tune the network parameters.Experiments have proved that the improved method of this article combined with DCC is superior to most classic clustering algorithms in clustering experiments,and it has also made significant progress in fine-grained classification experiments for real images.
2022, Vol. 43 
