Abstract: Sketch retrieval (SBIR) is an extension of content-based image retrieval (CBIR), which is a flexible and convenient way to retrieve target images. How to minimize the difference between the sketch domain and the image domain is crucial to SBIR. The traditional methods extract the manual features to achieve the approximate conversion between the sketch field and the image field, so as to reduce the domain difference. However, these methods cannot effectively fit the content of the two domains, resulting in low retrieval accuracy. Deep learning methods break through the limitations of traditional methods, which extract high-dimensional features from a large amount of data and have been proved to effectively solve the cross-domain modeling problems. This paper focuses on deep learning-based sketch retrieval methods, and covers several aspects such as the deep feature extraction model, public dataset, coarse-grained and fine-grained retrieval based on deep learning, deep hashing technology, category generalization, etc. Related works are reviewed and commented on. Then, a comparative experiment is conducted. For one hand, three existing public SBIR datasets such as Sketchy, TU-Berlin and QuickDraw are used for suitability evaluation. For the other hand, three latest SBIR deep learning models such as GRLZS model, SEM-PCYC model and SAKE model are selected for performance analysis and comparison. Finally, current challenges and future research trends of SBIR are summarized.

Key words: deep learning, sketch retrieval, feature extraction, content-based image retrieval