# Image Retrieval and Pattern Spotting using Siamese Neural Network

Source: [Image Retrieval and Pattern Spotting using Siamese Neural Network](<https://www.sota2.com/research/paper/68fa65f81e209131cea0d907>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:40:30.056Z

Authors: Kelly L. Wiggers, Alceu S. Britto Jr., Laurent Heutte, Alessandro L. Koerich, Luiz S. Oliveira

Published: 2019-06-22T22:33:44.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1906.09513>)

[PDF](<https://arxiv.org/pdf/1906.09513.pdf>)