Recent developments of content-based image retrieval (CBIR)
X Li, J Yang, J Ma - Neurocomputing, 2021 - Elsevier
With the development of Internet technology and the popularity of digital devices, Content-
Based Image Retrieval (CBIR) has been quickly developed and applied in various fields …
Based Image Retrieval (CBIR) has been quickly developed and applied in various fields …
Recent advance in content-based image retrieval: A literature survey
The explosive increase and ubiquitous accessibility of visual data on the Web have led to
the prosperity of research activity in image search or retrieval. With the ignorance of visual …
the prosperity of research activity in image search or retrieval. With the ignorance of visual …
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 …
Deep multi-view enhancement hashing for image retrieval
Hashing is an efficient method for nearest neighbor search in large-scale data space by
embedding high-dimensional feature descriptors into a similarity preserving Hamming …
embedding high-dimensional feature descriptors into a similarity preserving Hamming …
Deep fuzzy hashing network for efficient image retrieval
Hashing methods for efficient image retrieval aim at learning hash functions that map similar
images to semantically correlated binary codes in the Hamming space with similarity well …
images to semantically correlated binary codes in the Hamming space with similarity well …
Central similarity quantization for efficient image and video retrieval
Existing data-dependent hashing methods usually learn hash functions from pairwise or
triplet data relationships, which only capture the data similarity locally, and often suffer from …
triplet data relationships, which only capture the data similarity locally, and often suffer from …
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 …
Hashnet: Deep learning to hash by continuation
Learning to hash has been widely applied to approximate nearest neighbor search for large-
scale multimedia retrieval, due to its computation efficiency and retrieval quality. Deep …
scale multimedia retrieval, due to its computation efficiency and retrieval quality. Deep …
Deep supervised hashing for fast image retrieval
In this paper, we present a new hashing method to learn compact binary codes for highly
efficient image retrieval on large-scale datasets. While the complex image appearance …
efficient image retrieval on large-scale datasets. While the complex image appearance …
[PDF][PDF] 深度卷积神经网络的发展及其在计算机视觉领域的应用
张顺， 龚怡宏， 王进军 - 计算机学报, 2019 - cjc.ict.ac.cn
2)(西安交通大学人工智能与机器人研究所, 陕西西安, 710049) 摘要作为类脑计算领域的一个
重要研究成果, 深度卷积神经网络已经广泛应用到计算机视觉, 自然语言处理, 信息检索 …
重要研究成果, 深度卷积神经网络已经广泛应用到计算机视觉, 自然语言处理, 信息检索 …