# Deep Sketch Hashing: Fast Free-hand Sketch-Based Image Retrieval

arXiv: https://arxiv.org/abs/1703.05605 · HTML: https://mlsanity.com/paper/1703.05605/

## Key facts

- Cited by 68 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2017.

## Built on by (most cited first, 30 of 68)

- [Pic2Word: Mapping Pictures to Words for Zero-shot Composed Image Retrieval](https://mlsanity.com/paper/2302.03084/) (2023)
- [CLIP for All Things Zero-Shot Sketch-Based Image Retrieval, Fine-Grained or Not](https://mlsanity.com/paper/2303.13440/) (2023)
- [StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retrieval](https://mlsanity.com/paper/2103.15706/) (2021)
- [Sketch Your Own GAN](https://mlsanity.com/paper/2108.02774/) (2021)
- [Sketch3T: Test-Time Training for Zero-Shot SBIR](https://mlsanity.com/paper/2203.14691/) (2022)
- [Sketching without Worrying: Noise-Tolerant Sketch-Based Image Retrieval](https://mlsanity.com/paper/2203.14817/) (2022)
- [Zero-Shot Everything Sketch-Based Image Retrieval, and in Explainable Style](https://mlsanity.com/paper/2303.14348/) (2023)
- [Adversarial Open Domain Adaptation for Sketch-to-Photo Synthesis](https://mlsanity.com/paper/2104.05703/) (2021)
- [Doodle It Yourself: Class Incremental Learning by Drawing a Few Sketches](https://mlsanity.com/paper/2203.14843/) (2022)
- [An Efficient Framework for Zero-Shot Sketch-Based Image Retrieval](https://mlsanity.com/paper/2102.04016/) (2021)
- [Adaptive Fine-Grained Sketch-Based Image Retrieval](https://mlsanity.com/paper/2207.01723/) (2022)
- [Exploiting Unlabelled Photos for Stronger Fine-Grained SBIR](https://mlsanity.com/paper/2303.13779/) (2023)
- [Domain-Smoothing Network for Zero-Shot Sketch-Based Image Retrieval](https://mlsanity.com/paper/2106.11841/) (2021)
- [Operator SVD with Neural Networks via Nested Low-Rank Approximation](https://mlsanity.com/paper/2402.03655/) (2024)
- [Telling the What while Pointing to the Where: Multimodal Queries for Image Retrieval](https://mlsanity.com/paper/2102.04980/) (2021)
- [You'll Never Walk Alone: A Sketch and Text Duet for Fine-Grained Image Retrieval](https://mlsanity.com/paper/2403.07222/) (2024)
- [How to Handle Sketch-Abstraction in Sketch-Based Image Retrieval?](https://mlsanity.com/paper/2403.07203/) (2024)
- [CrossATNet - A Novel Cross-Attention Based Framework for Sketch-Based Image Retrieval](https://mlsanity.com/paper/2104.09918/) (2021)
- [SHREC'22 Track: Sketch-Based 3D Shape Retrieval in the Wild](https://mlsanity.com/paper/2207.04945/) (2022)
- [dualFace:Two-Stage Drawing Guidance for Freehand Portrait Sketching](https://mlsanity.com/paper/2104.12297/) (2021)
- [BDA-SketRet: Bi-Level Domain Adaptation for Zero-Shot SBIR](https://mlsanity.com/paper/2201.06570/) (2022)
- [Text-to-Image Diffusion Models are Great Sketch-Photo Matchmakers](https://mlsanity.com/paper/2403.07214/) (2024)
- [Zero-Shot Sketch Based Image Retrieval using Graph Transformer](https://mlsanity.com/paper/2201.10185/) (2022)
- [Structure-Aware 3D VR Sketch to 3D Shape Retrieval](https://mlsanity.com/paper/2209.09043/) (2022)
- [Content-Based Search for Deep Generative Models](https://mlsanity.com/paper/2210.03116/) (2022)
- [Data-Free Sketch-Based Image Retrieval](https://mlsanity.com/paper/2303.07775/) (2023)
- [AniFaceDrawing: Anime Portrait Exploration during Your Sketching](https://mlsanity.com/paper/2306.07476/) (2023)
- [Universal Cross-Domain Retrieval: Generalizing Across Classes and Domains](https://mlsanity.com/paper/2108.08356/) (2021)
- [ProS: Prompting-to-simulate Generalized knowledge for Universal Cross-Domain Retrieval](https://mlsanity.com/paper/2312.12478/) (2023)
- [Sketch-QNet: A Quadruplet ConvNet for Color Sketch-based Image Retrieval](https://mlsanity.com/paper/2104.11130/) (2021)

## Abstract (arXiv)

Free-hand sketch-based image retrieval (SBIR) is a specific cross-view retrieval task, in which queries are abstract and ambiguous sketches while the retrieval database is formed with natural images. Work in this area mainly focuses on extracting representative and shared features for sketches and natural images. However, these can neither cope well with the geometric distortion between sketches and images nor be feasible for large-scale SBIR due to the heavy continuous-valued distance computation. In this paper, we speed up SBIR by introducing a novel binary coding method, named Deep Sketch Hashing (DSH), where a semi-heterogeneous deep architecture is proposed and incorporated into an end-to-end binary coding framework. Specifically, three convolutional neural networks are utilized to encode free-hand sketches, natural images and, especially, the auxiliary sketch-tokens which are adopted as bridges to mitigate the sketch-image geometric distortion. The learned DSH codes can effectively capture the cross-view similarities as well as the intrinsic semantic correlations between different categories. To the best of our knowledge, DSH is the first hashing work specifically designed for category-level SBIR with an end-to-end deep architecture. The proposed DSH is comprehensively evaluated on two large-scale datasets of TU-Berlin Extension and Sketchy, and the experiments consistently show DSH's superior SBIR accuracies over several state-of-the-art methods, while achieving significantly reduced retrieval time and memory footprint.