# Embedding based on function approximation for large scale image search

arXiv: https://arxiv.org/abs/1605.06914 · HTML: https://mlsanity.com/paper/1605.06914/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2016.

## Built on by (most cited first, 5 of 5)

- [Deep Learning for Instance Retrieval: A Survey](https://mlsanity.com/paper/2101.11282/) (2021)
- [Medical Image Retrieval via Nearest Neighbor Search on Pre-trained Image Features](https://mlsanity.com/paper/2210.02401/) (2022)
- [Exemplar-Based Image Colorization with A Learning Framework](https://mlsanity.com/paper/2209.05775/) (2022)
- [MARs: Multi-view Attention Regularizations for Patch-based Feature Recognition of Space Terrain](https://mlsanity.com/paper/2410.05182/) (2024)
- [Retrieval Augmented Image Harmonization](https://mlsanity.com/paper/2412.13916/) (2024)

## Abstract (arXiv)

The objective of this paper is to design an embedding method that maps local features describing an image (e.g. SIFT) to a higher dimensional representation useful for the image retrieval problem. First, motivated by the relationship between the linear approximation of a nonlinear function in high dimensional space and the stateof-the-art feature representation used in image retrieval, i.e., VLAD, we propose a new approach for the approximation. The embedded vectors resulted by the function approximation process are then aggregated to form a single representation for image retrieval. Second, in order to make the proposed embedding method applicable to large scale problem, we further derive its fast version in which the embedded vectors can be efficiently computed, i.e., in the closed-form. We compare the proposed embedding methods with the state of the art in the context of image search under various settings: when the images are represented by medium length vectors, short vectors, or binary vectors. The experimental results show that the proposed embedding methods outperform existing the state of the art on the standard public image retrieval benchmarks.