Unsupervised Rank-Preserving Hashing for Large-Scale Image Retrieval

03/04/2019
by   Svebor Karaman, et al.
0

We propose an unsupervised hashing method which aims to produce binary codes that preserve the ranking induced by a real-valued representation. Such compact hash codes enable the complete elimination of real-valued feature storage and allow for significant reduction of the computation complexity and storage cost of large-scale image retrieval applications. Specifically, we learn a neural network-based model, which transforms the input representation into a binary representation. We formalize the training objective of the network in an intuitive and effective way, considering each training sample as a query and aiming to obtain the same retrieval results using the produced hash codes as those obtained with the original features. This training formulation directly optimizes the hashing model for the target usage of the hash codes it produces. We further explore the addition of a decoder trained to obtain an approximated reconstruction of the original features. At test time, we retrieved the most promising database samples with an efficient graph-based search procedure using only our hash codes and perform re-ranking using the reconstructed features, thus without needing to access the original features at all. Experiments conducted on multiple publicly available large-scale datasets show that our method consistently outperforms all compared state-of-the-art unsupervised hashing methods and that the reconstruction procedure can effectively boost the search accuracy with a minimal constant additional cost.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
02/28/2017

Unsupervised Triplet Hashing for Fast Image Retrieval

Hashing has played a pivotal role in large-scale image retrieval. With t...
research
03/17/2016

Variable-Length Hashing

Hashing has emerged as a popular technique for large-scale similarity se...
research
08/23/2023

HashReID: Dynamic Network with Binary Codes for Efficient Person Re-identification

Biometric applications, such as person re-identification (ReID), are oft...
research
07/18/2017

Hashed Binary Search Sampling for Convolutional Network Training with Large Overhead Image Patches

Very large overhead imagery associated with ground truth maps has the po...
research
03/08/2018

Learning Effective Binary Visual Representations with Deep Networks

Although traditionally binary visual representations are mainly designed...
research
10/13/2017

End-to-end Network for Twitter Geolocation Prediction and Hashing

We propose an end-to-end neural network to predict the geolocation of a ...
research
11/20/2020

Shuffle and Learn: Minimizing Mutual Information for Unsupervised Hashing

Unsupervised binary representation allows fast data retrieval without an...

Please sign up or login with your details

Forgot password? Click here to reset