Abstract: For image clustering, the existing methods are either difficult to choose the appropriate dimension transformation method in feature extraction, or the extracted features are weak and not rich enough for the expression of image features, which have a great impact on the clustering effect of images and lead to low clustering accuracy. Therefore, an image clustering method based on multi-scale residual convolutional autoencoder is proposed. By constructing several multi-scale convolutional modules with residual connections, the high-dimensional feature expression of the middle layer is obtained, and the image is clustered based on these features. The clustering accuracy on MNIST data set is 82. 2% , ARI (Adjusted Rand Index) value is 0. 781 0 and NMI ( Normalized Mutual Information) value is 0. 853 2, indicating that the model has achieved good clustering effect.

Key words: convolutional autoencoder； , image clustering； , multi-scale residual connection； , deep learning