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 …
Learning to hash: a comprehensive survey of deep learning-based hashing methods
A Singh, S Gupta - Knowledge and Information Systems, 2022 - Springer
Explosive growth of big data demands efficient and fast algorithms for nearest neighbor
search. Deep learning-based hashing methods have proved their efficacy to learn advanced …
search. Deep learning-based hashing methods have proved their efficacy to learn advanced …
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 …
Weakly-supervised semantic guided hashing for social image retrieval
Hashing has been widely investigated for large-scale image retrieval due to its search
effectiveness and computation efficiency. In this work, we propose a novel Semantic Guided …
effectiveness and computation efficiency. In this work, we propose a novel Semantic Guided …
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 …
Greedy hash: Towards fast optimization for accurate hash coding in cnn
To convert the input into binary code, hashing algorithm has been widely used for
approximate nearest neighbor search on large-scale image sets due to its computation and …
approximate nearest neighbor search on large-scale image sets due to its computation and …
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 …
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 unsupervised image hashing by maximizing bit entropy
Unsupervised hashing is important for indexing huge image or video collections without
having expensive annotations available. Hashing aims to learn short binary codes for …
having expensive annotations available. Hashing aims to learn short binary codes for …
Deep learning for approximate nearest neighbour search: A survey and future directions
Approximate nearest neighbour search (ANNS) in high-dimensional space is an essential
and fundamental operation in many applications from many domains such as multimedia …
and fundamental operation in many applications from many domains such as multimedia …