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A decade survey of content based image retrieval using deep learning
SR Dubey - IEEE Transactions on Circuits and Systems for …, 2021 - ieeexplore.ieee.org
The content based image retrieval aims to find the similar images from a large scale dataset
against a query image. Generally, the similarity between the representative features of the …
against a query image. Generally, the similarity between the representative features of the …
One loss for all: Deep hashing with a single cosine similarity based learning objective
A deep hashing model typically has two main learning objectives: to make the learned
binary hash codes discriminative and to minimize a quantization error. With further …
binary hash codes discriminative and to minimize a quantization error. With further …
A survey on deep hashing methods
Nearest neighbor search aims at obtaining the samples in the database with the smallest
distances from them to the queries, which is a basic task in a range of fields, including …
distances from them to the queries, which is a basic task in a range of fields, including …
Deep hashing with minimal-distance-separated hash centers
Deep hashing is an appealing approach for large-scale image retrieval. Most existing
supervised deep hashing methods learn hash functions using pairwise or triple image …
supervised deep hashing methods learn hash functions using pairwise or triple image …
Auto-encoding twin-bottleneck hashing
Conventional unsupervised hashing methods usually take advantage of similarity graphs,
which are either pre-computed in the high-dimensional space or obtained from random …
which are either pre-computed in the high-dimensional space or obtained from random …
One loss for quantization: Deep hashing with discrete wasserstein distributional matching
Image hashing is a principled approximate nearest neighbor approach to find similar items
to a query in a large collection of images. Hashing aims to learn a binary-output function that …
to a query in a large collection of images. Hashing aims to learn a binary-output function that …
Self-supervised product quantization for deep unsupervised image retrieval
Supervised deep learning-based hash and vector quantization are enabling fast and large-
scale image retrieval systems. By fully exploiting label annotations, they are achieving …
scale image retrieval systems. By fully exploiting label annotations, they are achieving …
Scalable deep hashing for large-scale social image retrieval
Recent years have witnessed the wide application of hashing for large-scale image retrieval,
because of its high computation efficiency and low storage cost. Particularly, benefiting from …
because of its high computation efficiency and low storage cost. Particularly, benefiting from …
Unsupervised hashing with contrastive information bottleneck
Many unsupervised hashing methods are implicitly established on the idea of reconstructing
the input data, which basically encourages the hashing codes to retain as much information …
the input data, which basically encourages the hashing codes to retain as much information …
[PDF][PDF] Deep Polarized Network for Supervised Learning of Accurate Binary Hashing Codes.
This paper proposes a novel deep polarized network (DPN) for learning to hash, in which
each channel in the network outputs is pushed far away from zero by employing a …
each channel in the network outputs is pushed far away from zero by employing a …