# Image Reconstruction from Bag-of-Visual-Words

arXiv: https://arxiv.org/abs/1505.05190 · HTML: https://mlsanity.com/paper/1505.05190/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2015.

## Built on by (most cited first, 6 of 6)

- [Zero-Shot Everything Sketch-Based Image Retrieval, and in Explainable Style](https://mlsanity.com/paper/2303.14348/) (2023)
- [NinjaDesc: Content-Concealing Visual Descriptors via Adversarial Learning](https://mlsanity.com/paper/2112.12785/) (2021)
- [How Privacy-Preserving are Line Clouds? Recovering Scene Details from 3D Lines](https://mlsanity.com/paper/2103.05086/) (2021)
- [Analysis and Mitigations of Reverse Engineering Attacks on Local Feature Descriptors](https://mlsanity.com/paper/2105.03812/) (2021)
- [LDP-Feat: Image Features with Local Differential Privacy](https://mlsanity.com/paper/2308.11223/) (2023)
- [Obfuscation Based Privacy Preserving Representations are Recoverable Using Neighborhood Information](https://mlsanity.com/paper/2409.11536/) (2024)

## Abstract (arXiv)

The objective of this work is to reconstruct an original image from Bag-of-Visual-Words (BoVW). Image reconstruction from features can be a means of identifying the characteristics of features. Additionally, it enables us to generate novel images via features. Although BoVW is the de facto standard feature for image recognition and retrieval, successful image reconstruction from BoVW has not been reported yet. What complicates this task is that BoVW lacks the spatial information for including visual words. As described in this paper, to estimate an original arrangement, we propose an evaluation function that incorporates the naturalness of local adjacency and the global position, with a method to obtain related parameters using an external image database. To evaluate the performance of our method, we reconstruct images of objects of 101 kinds. Additionally, we apply our method to analyze object classifiers and to generate novel images via BoVW.