Sketch-Based Cross-Domain Image Retrieval Via Heterogeneous Network

The development of image diversity has led multiple fields' application of cross-domain image retrieval. In this paper, we propose a heterogeneous dual network (two different networks) based on sketches and images. End-to-end cross-domain image retrieval is realized by limiting the similarity of features extracted from the two networks through the method of combining the contrastive loss with the triplet ranking loss. We also study how the order of drawings affects the sketch retrieval. Compared to the Siamese network, we avoid edge extraction for preprocessing, and the effect of fine-grained retrieval is further enhanced.