# Image Retrieval and Pattern Spotting using Siamese Neural Network

arXiv: https://arxiv.org/abs/1906.09513 · HTML: https://mlsanity.com/paper/1906.09513/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2019.

## Built on by (most cited first, 2 of 2)

- [Neural ranking models for document retrieval](https://mlsanity.com/paper/2102.11903/) (2021)
- [Measuring Similarity between Artistic and AI Generated Images using Siamese Neural Networks](https://mlsanity.com/paper/2608.28671/) (2026)

## Abstract (arXiv)

This paper presents a novel approach for image retrieval and pattern spotting in document image collections. The manual feature engineering is avoided by learning a similarity-based representation using a Siamese Neural Network trained on a previously prepared subset of image pairs from the ImageNet dataset. The learned representation is used to provide the similarity-based feature maps used to find relevant image candidates in the data collection given an image query. A robust experimental protocol based on the public Tobacco800 document image collection shows that the proposed method compares favorably against state-of-the-art document image retrieval methods, reaching 0.94 and 0.83 of mean average precision (mAP) for retrieval and pattern spotting (IoU=0.7), respectively. Besides, we have evaluated the proposed method considering feature maps of different sizes, showing the impact of reducing the number of features in the retrieval performance and time-consuming.