# Image Retrieval based on Bag-of-Words model

Source: [Image Retrieval based on Bag-of-Words model](<https://www.sota2.com/research/paper/68fa60dc1e209131ce9f1908>)

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

Data updated: 2025-10-25T19:48:33.705Z

Authors: Jialu Liu

Published: 2013-04-18T15:57:34.000Z

## Abstract

This article gives a survey for bag-of-words (BoW) or bag-of-features model in image retrieval system. In recent years, large-scale image retrieval shows significant potential in both industry applications and research problems. As local descriptors like SIFT demonstrate great discriminative power in solving vision problems like object recognition, image classification and annotation, more and more state-of-the-art large scale image retrieval systems are trying to rely on them. A common way to achieve this is first quantizing local descriptors into visual words, and then applying scalable textual indexing and retrieval schemes. We call this model as bag-of-words or bag-of-features model. The goal of this survey is to give an overview of this model and introduce different strategies when building the system based on this model.

## 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/1304.5168>)

[PDF](<https://arxiv.org/pdf/1304.5168.pdf>)