Edge-Guided Cross-Domain Learning with Shape Regression for Sketch-Based Image Retrieval

Abstract

Sketch-based image retrieval (SBIR) is a challenging task due to the cross-domain gap between sketch queries and natural images. In this paper, we propose a novel edge-guided cross-domain learning network to reduce the domain gap. In particular, edge maps extracted from natural images are introduced as the bridge between two domains, and the inputs from different domains are embedded into a common feature space. An edge guidance module is proposed to fuse natural images and the corresponding edge maps, which guides the network to generate more discriminative features in the domain alignment process. Meanwhile, a shape regression module is proposed to capture the inherent shape similarity between sketches and natural images. By training the proposed network in an end-to-end process, the sketch and natural image domains can be effectively associated, which potentially overcomes the challenge of the common feature learning for two heterogeneous domains. The experimental results on the SBIR dataset demonstrate that the proposed method achieves superior performance compared with the state-of-the-art methods.