Image deep clustering based on local-topology embedding

Abstract

• The feature representation consists of two parts: data and its local-topology information. • We propose a replacement strategy to find local-topology representation of data. • Data augmentation is applied to improve the generalization performance of the model. • A two-stage image deep clustering algorithm is presented based on local-topology embedding called ITEC. Reasonable feature representation plays an important role in improving the performance of clustering algorithms. However, recent deep clustering studies only focusing on feature representation at the pixel level leads to feature representation with low discrimination. Our key insight is that considering local-topology information between images would help to get a highly discriminative representation, and therefore we design a replacement strategy to find local-topology representation of data, and propose a two-stage image deep clustering algorithm based on local-topology embedding called ITEC. Specifically, we take advantage of data augmentation technique to improve the generalization performance of the learning models; then local-topology representation of data is embedded into the representation of data itself, so as to better complete tasks of image clustering. Extensive experiments demonstrate that local-topology information effectively promotes the performance of deep clustering significantly.