# Asymmetric Feature Maps with Application to Sketch Based Retrieval

arXiv: https://arxiv.org/abs/1704.03946 · HTML: https://mlsanity.com/paper/1704.03946/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2017.

## Built on by (most cited first, 8 of 8)

- [StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retrieval](https://mlsanity.com/paper/2103.15706/) (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)
- [Abstracting Sketches through Simple Primitives](https://mlsanity.com/paper/2207.13543/) (2022)
- [Edge Augmentation for Large-Scale Sketch Recognition without Sketches](https://mlsanity.com/paper/2202.13164/) (2022)
- [Multi-granularity Association Learning Framework for on-the-fly Fine-Grained Sketch-based Image Retrieval](https://mlsanity.com/paper/2201.05007/) (2022)
- [Towards Practicality of Sketch-Based Visual Understanding](https://mlsanity.com/paper/2210.15146/) (2022)
- [Co-Segmentation without any Pixel-level Supervision with Application to Large-Scale Sketch Classification](https://mlsanity.com/paper/2410.13582/) (2024)

## Abstract (arXiv)

We propose a novel concept of asymmetric feature maps (AFM), which allows to evaluate multiple kernels between a query and database entries without increasing the memory requirements. To demonstrate the advantages of the AFM method, we derive a short vector image representation that, due to asymmetric feature maps, supports efficient scale and translation invariant sketch-based image retrieval. Unlike most of the short-code based retrieval systems, the proposed method provides the query localization in the retrieved image. The efficiency of the search is boosted by approximating a 2D translation search via trigonometric polynomial of scores by 1D projections. The projections are a special case of AFM. An order of magnitude speed-up is achieved compared to traditional trigonometric polynomials. The results are boosted by an image-based average query expansion, exceeding significantly the state of the art on standard benchmarks.