Unsupervised Deep-Embedding Global Feature Descriptor for Image Retrieval

Image representations based on deep learning models can provide exciting performance for image retrieval, but only using deep learning models cannot exploit global topological properties appropriately. The topological perception theory claims that the visual perception process is from global to local: global topological perception occurs earlier than other local patterns. Simulating the visual perception mechanism together with deep learning models to provide a compact yet discriminative representation remains challenging. Toward this end, we propose a novel image representation method called deep-embedding global feature descriptor. The main highlights include: (1) A frequency statistics ranking method is proposed to yield global topology features by combining global visual features and deep convolutional features. (2) An embedding method is proposed to embed the global topology feature spatially and channel-wise into deep convolutional features. It can reasonably integrate the global topological characteristic with local patterns by simulating the visual perceptual process from global to local. (3) A compact yet discriminative representation is provided by leveraging the advantages of global visual and deep features. Exhaustive experiments on five well-known benchmark datasets show that the proposed method outperforms some recent unsupervised state-of-the-art methods.