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 …
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 …
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
Y Li, J van Gemert - Proceedings of the AAAI Conference on Artificial …, 2021 - ojs.aaai.org
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 …