# Image Retrieval with Fisher Vectors of Binary Features

Source: [Image Retrieval with Fisher Vectors of Binary Features](<https://www.sota2.com/research/paper/68fa62bc1e209131ce9fafd9>)

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

Data updated: 2025-10-27T05:51:52.529Z

Authors: Yusuke Uchida, Shigeyuki Sakazawa, Shin'ichi Satoh

Published: 2016-09-27T07:33:58.000Z

## Abstract

Recently, the Fisher vector representation of local features has attracted much attention because of its effectiveness in both image classification and image retrieval. Another trend in the area of image retrieval is the use of binary features such as ORB, FREAK, and BRISK. Considering the significant performance improvement for accuracy in both image classification and retrieval by the Fisher vector of continuous feature descriptors, if the Fisher vector were also to be applied to binary features, we would receive similar benefits in binary feature based image retrieval and classification. In this paper, we derive the closed-form approximation of the Fisher vector of binary features modeled by the Bernoulli mixture model. We also propose accelerating the Fisher vector by using the approximate value of posterior probability. Experiments show that the Fisher vector representation significantly improves the accuracy of image retrieval compared with a bag of binary words approach.

## 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/1609.08291>)

[PDF](<https://arxiv.org/pdf/1609.08291.pdf>)