Improved image clustering with deep semantic embedding

Dimensionality reduction has found extensive use in the area of high dimensional data clustering. But unfortunately some valuable discriminative information might be lost in the process of dimensionality reduction, including feature extraction and feature selection. This issue inevitably degrades the performance of clustering algorithms. Recently, the semantic space embedding is emerging as a promising technique for state-of-the-art clustering methods, which can provide extra discriminative information and reasonably improve the clustering performance. In this paper, we plan to improve the performance of high dimensional image clustering by embedding semantic information into the original visual space. Inspired by the great success of deep learning, we employed a multi-layer autoencoder based on deep neural networks (DNNs) to undertake the semantic feature embedding and dimensionality reduction. By this way, the final image clustering task is carried out in the lower-dimensional feature space with deep semantic embedding. A series of experiments on acknowledged benchmark image datasets demonstrate that the proposed approaches can achieve superior performance over several existing clustering methods. (C) 2018 Elsevier B.V. All rights reserved.