# Compact and Effective Representations for Sketch-based Image Retrieval

arXiv: https://arxiv.org/abs/2104.10278 · HTML: https://mlsanity.com/paper/2104.10278/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 20 Apr 2021.

## Builds on (most cited first, 4 of 4)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Deep Sketch Hashing: Fast Free-hand Sketch-Based Image Retrieval](https://mlsanity.com/paper/1703.05605/) (2017)
- [Deep Supervised Hashing with Triplet Labels](https://mlsanity.com/paper/1612.03900/) (2016)

## Built on by (most cited first, 3 of 3)

- [Degradation Modeling and Prognostic Analysis Under Unknown Failure Modes](https://mlsanity.com/paper/2402.19294/) (2024)
- [ACNet: Approaching-and-Centralizing Network for Zero-Shot Sketch-Based Image Retrieval](https://mlsanity.com/paper/2111.12757/) (2021)
- [Leveraging Unlabeled Data for Sketch-based Understanding](https://mlsanity.com/paper/2204.12522/) (2022)

## Abstract (arXiv)

Sketch-based image retrieval (SBIR) has undergone an increasing interest in the community of computer vision bringing high impact in real applications. For instance, SBIR brings an increased benefit to eCommerce search engines because it allows users to formulate a query just by drawing what they need to buy. However, current methods showing high precision in retrieval work in a high dimensional space, which negatively affects aspects like memory consumption and time processing. Although some authors have also proposed compact representations, these drastically degrade the performance in a low dimension. Therefore in this work, we present different results of evaluating methods for producing compact embeddings in the context of sketch-based image retrieval. Our main interest is in strategies aiming to keep the local structure of the original space. The recent unsupervised local-topology preserving dimension reduction method UMAP fits our requirements and shows outstanding performance, improving even the precision achieved by SOTA methods. We evaluate six methods in two different datasets. We use Flickr15K and eCommerce datasets; the latter is another contribution of this work. We show that UMAP allows us to have feature vectors of 16 bytes improving precision by more than 35%.