2024 Graph-based supervised discrete image hashing

Graph-based supervised discrete image hashing

Author: clsg

August undefined, 2024

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 ... 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 …

Supervised Discrete Hashing IEEE Conference Publication …

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. ... 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 … jillian poundstone facebook

Supervised Discrete Hashing IEEE Conference Publication IEEE Xplore

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 ... 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 … 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 … jillian powers fau

Webly Supervised Image Hashing with Lightweight Semantic …

Semi-supervised Cross-Modal Hashing with Graph Convolutional …

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 … 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 … installing shaw direct satellite dishWebApr 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 ... installing sharepoint 2013 designer

"WebJan 1, 2024 · A graph-based supervised discrete hashing approach is proposed, which can better preserve the data property by maintaining both the locality manifold … " - Graph-based supervised discrete image hashing

Graph-based supervised discrete image hashing

Online unsupervised cross-view discrete hashing for large-scale ...

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 ... 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 …

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WebOct 15, 2024 · In [ 48 ], Yang et al. proposed a Feature Pyramid Hashing (FPH) as a two-pyramids (vertical and horizontal) image hashing architecture to learn the subtle appearance details and the semantic information for fine-grained image retrieval. Ng et al. [ 49] developed a novel multi-level supervised hashing (MLSH) technique for image … WebDiscrete Binary Hashing Towards Efficient Fashion Recommendation. Authors: Luyao Liu ...

WebDec 31, 2024 · Graph-Based Supervised Discrete Image Hashing. ... In this paper, we propose a graph-based supervised hashing framework to address these problems, … 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 ...

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 … 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 ...

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 …

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 … installing shaw luxury vinyl plankWebDec 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 … jillian reed liberty mutualWebDec 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 … jillian pritchard cookeWebDec 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 … installing sheathed cable connectorsWebApr 27, 2024 · Hashing methods have received significant attention for effective and efficient large scale similarity search in computer vision and information retrieval … jillian riccio american towerWebpaper 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 … jillian rice eyWebJun 12, 2015 · We evaluate the proposed approach, dubbed Supervised Discrete Hashing (SDH), on four large image datasets and demonstrate its superiority to the state-of-the … jillian prather dds owasso