Graph-based supervised discrete image hashing

Author: xsyu

August undefined, 2024

WebIn recent years, supervised hashing has been validated to greatly boost the performance of image retrieval. However, the label-hungry property requires massive label collection, making it intractable in practical scenarios. To liberate the model training procedure from laborious manual annotations, some unsupervised methods are proposed. However, the … WebKernel-based supervised hashing (KSH) [40] ... training the model to predict the learned hash codes as well as the discrete image class labels. Deep Cauchy hashing (DCH) [5] adopts Cauchy distribution to continue to opti- ... Discrete graph hashing (DGH) [39] casts the graph hashing problem into a discrete optimization framework and explic-

Discrete Graph Hashing - NeurIPS

Webdubbed Supervised Discrete Hashing (SDH), on four large image datasets and demonstrate its superiority to the state-of-the-art hashing methods in large-scale image … WebLearning Discrete Class-specific Prototypes for Deep Semantic Hashing. Deep supervised hashing methods have become popular for large-scale image retrieval tasks. Recently, some deep supervised hashing methods have utilized the semantic clustering of hash codes to improve their semantic discriminative ability and polymerization. However, there ... granted wish dispatching llc

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WebDec 8, 2014 · This paper presents a graph-based unsupervised hashing model to preserve the neighborhood structure of massive data in a discrete code space. We cast … WebOct 12, 2024 · To address this issue, this work proposes a novel Masked visual-semantic Graph-based Reasoning Network, termed as MGRN, to learn joint visual-semantic … WebDec 1, 2024 · In this paper, we propose a novel supervised hashing method, called latent factor hashing(LFH), to learn similarity-preserving binary codes based on latent factor … granted wealth

Supervised Discrete Hashing - cv-foundation.org

Information Retrieval Research Topics for MS PhD

WebAug 1, 2024 · However, many existing hashing methods cannot perform well on large-scale social image retrieval, due to the relaxed hash optimization and the lack of supervised semantic labels. In this paper, we ... grant edwards organistWebEfficient weakly-supervised discrete hashing for large-scale social image retrieval; ... M-GCN: Multi-branch graph convolution network for 2D image-based on 3D model retrieval; The Mediation Effect of Management Information Systems on the Relationship between Big Data Quality and Decision making Quality; chip and joanna gaines recipes

"WebAs satellite observation technology rapidly develops, the number of remote sensing (RS) images dramatically increases, and this leads RS image retrieval tasks to be more challenging in terms of speed and accuracy. Recently, an increasing number of researchers have turned their attention to this issue, as well as hashing algorithms, which map real … " - Graph-based supervised discrete image hashing

Graph-based supervised discrete image hashing

WebFeb 8, 2024 · In this paper, we have proposed a new type of unsupervised hashing method called sparse graph based self-supervised hashing to address the existing problems in image retrieval tasks. Unlike conventional dense graph- and anchor graph-based hashing methods that use a full connection graph, with our method, a sparse graph is built to … WebJan 6, 2024 · This work proposes a hashing algorithm based on auto-encoders for multiview binary clustering, which dynamically learns affinity graphs with low-rank …

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WebDec 5, 2024 · Abstract. Hashing has been widely used to approximate the nearest neighbor search for image retrieval due to its high computation efficiency and low storage requirement. With the development of deep learning, a series of deep supervised methods were proposed for end-to-end binary code learning. However, the similarity between … WebDiscrete Binary Hashing Towards Efficient Fashion Recommendation. Authors: Luyao Liu ...

Web3.1. Problem Setting. Suppose the database consists of streaming images. When new images come in, we update the hash functions. We define as image matrix, where is the number of all training images in database and is the dimension of image feature. In the online learning process, image matrix X can be represented as , where denotes old … Webing methods, such as Co-Regularized Hashing (CRH) [38], Supervised Matrix Factorization Hashing (SMFH) [27] and Discriminant Cross-modal Hashing (DCMH) [32], are de …

WebAs such, a high-quality discrete solution can eventually be obtained in an efficient computing manner, therefore enabling to tackle massive datasets. We evaluate the … WebJan 21, 2024 · To overcome these limitations, we propose a novel semi-supervised cross-modal graph convolutional network hashing (CMGCNH) method, which for the first time exploits asymmetric GCN architecture in scalable cross-modal retrieval tasks. Without loss of generality, in this paper, we concentrate on bi-modal (images and text) hashing, and …

WebSupervised hashing aims to map the original features to compact binary codes that are able to preserve label based similarity in the binary Hamming space. Most … To build …

WebScalable Graph Hashing with Feature Transformation. In IJCAI. 2248--2254. Google Scholar ... Zizhao Zhang, Yuanpu Xie, and Lin Yang. 2016. Kernel-based Supervised Discrete Hashing for Image Retrieval. In ECCV. 419--433. Google Scholar; Karen Simonyan and Andrew Zisserman. 2015. Very Deep Convolutional Networks for Large … chip and joanna gaines remodeled homesWebApr 27, 2024 · Hashing methods have received significant attention for effective and efficient large scale similarity search in computer vision and information retrieval community. However, most existing cross-view hashing methods mainly focus on either similarity preservation of data or cross-view correlation. In this paper, we propose a graph … chip and joanna gaines shop magnoliaWebIn this article, we propose a novel asymmetric hashing method, called Deep Uncoupled Discrete Hashing (DUDH), for large-scale approximate nearest neighbor search. Instead of directly preserving the similarity between the query and database, DUDH first exploits a small similarity-transfer image set to transfer the underlying semantic structures ... chip and joanna gaines rugsWebDec 31, 2016 · In this paper, we propose a novel supervised hashing method, i.e., Class Graph Preserving Hashing (CGPH), which can tackle both image retrieval and … chip and joanna gaines republicanWebstate-of-the-art unsupervised, semi-supervised, and super-vised hashing methods. 2. Kernel-Based Supervised Hashing 2.1. Hash Functions with Kernels Given a data set 𝒳= {𝒙1,⋅⋅⋅,𝒙𝑛}⊂ℝ𝑑, the pur-pose of hashing is to look for a group of appropriate hash functions ℎ: ℝ𝑑→{1,−1}1, each of which accounts for granted wish foundationWebApr 14, 2024 · The core is a new lighting model (DSGLight) based on depth-augmented spherical Gaussians (SGs) and a graph convolutional network (GCN) that infers the new lighting representation from a single low ... grant edwards psychologistWebFeb 13, 2024 · Abstract. Recently, many graph based hashing methods have been emerged to tackle large-scale problems. However, there exists two major bottlenecks: (1) directly learning discrete hashing codes is ... chip and joanna gaines renovation pictures