# Asymmetric Feature Maps with Application to Sketch Based Retrieval

## TL;DR

This paper introduces asymmetric feature maps (AFM) for efficient, scale and translation invariant sketch-based image retrieval, enabling multiple kernel evaluations without extra memory and providing query localization.

## Contribution

The paper presents a novel AFM approach that improves retrieval efficiency and accuracy, including a new image representation and a faster approximation method for translation search.

## Key findings

- Achieves an order of magnitude speed-up over traditional methods
- Outperforms state-of-the-art on standard benchmarks
- Provides query localization in retrieved images

## Abstract

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.