2024 Graph-based supervised discrete image hashing

Graph-based supervised discrete image hashing

Author: botu

August undefined, 2024

Webing methods, such as Co-Regularized Hashing (CRH) [38], Supervised Matrix Factorization Hashing (SMFH) [27] and Discriminant Cross-modal Hashing (DCMH) [32], are de … WebDec 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 classification tasks on large scale data. In CGPH, we firstly learn the hashing functions by simultaneously ensuring the label consistency and preserving the classes similarity …

Unsupervised Discrete Hashing with Affinity Similarity

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 … WebApr 27, 2024 · Hashing methods have received significant attention for effective and efficient large scale similarity search in computer vision and information retrieval … sierra canyon basketball vs glenbard west

Efficient Weakly-supervised Discrete Hashing for Large-scale …

WebFeb 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 ... 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 … Webpaper presents a graph-based unsupervised hashing model to preserve the neigh-borhood structure of massive data in a discrete code space. We cast the graph hashing … sierra canyon basketball bishop montgomery

Discrete graph hashing Proceedings of the 27th

Supervised Class Graph Preserving Hashing for Image Retrieval and ...

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 … 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 ... the power charactersWebApr 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 ... sierra canyon baseball schedule

"WebApr 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 … " - Graph-based supervised discrete image hashing

Graph-based supervised discrete image hashing

HHF: Hashing-guided Hinge Function for Deep Hashing …

WebDec 21, 2024 · In this paper, we propose a novel hashing method: online discrete anchor graph hashing (ODAGH) for mobile person re-id. ODAGH integrates the advantages of online learning and hashing technology. 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 …

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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 … WebFeb 18, 2024 · To fill this gap, this paper proposes a new online cross-view hashing method, dubbed online unsupervised cross-view discrete hashing (OUCDH) that considers similarity preservation and quantization ...

WebIn 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 ... 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 ...

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 … WebDiscrete Binary Hashing Towards Efficient Fashion Recommendation. Authors: Luyao Liu ...

WebEfficient 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;

WebDiscrete Graph Hashing Wei Liu, Cun Mu, Sanjiv Kumar and Shih-Fu Chang. [NIPS], 2014 ... Column sampling based discrete supervised hashing. Wang-Cheng Kang, Wu-Jun Li and Zhi-Hua Zhou. ... Deep Hashing; Supervised Hashing via Image Representation Learning Rongkai Xia , Yan Pan, Hanjiang Lai, Cong Liu, and Shuicheng Yan. ... the power cherWebJan 1, 2024 · A graph-based supervised discrete hashing approach is proposed, which can better preserve the data property by maintaining both the locality manifold … the power checkinWebDec 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 … the powerchair companyWebApr 9, 2024 · Hashing is very popular for remote sensing image search. This article proposes a multiview hashing with learnable parameters to retrieve the queried images for a large-scale remote sensing dataset. the power checkin textWebJan 6, 2024 · This work proposes a hashing algorithm based on auto-encoders for multiview binary clustering, which dynamically learns affinity graphs with low-rank … sierra canyon bell scheduleWebdubbed 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 … sierra canyon basketball bronnyWebSupervised 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 … sierra canyon bishop amat