Results 161 to 170 of about 80,492 (210)

Deep Joint-Semantics Reconstructing Hashing for Large-Scale Unsupervised Cross-Modal Retrieval

IEEE International Conference on Computer Vision, 2019
Cross-modal hashing encodes the multimedia data into a common binary hash space in which the correlations among the samples from different modalities can be effectively measured.
Shupeng Su, Zhisheng Zhong, Chao Zhang
semanticscholar   +1 more source

Deep Cross-Modal Hashing

Computer Vision and Pattern Recognition, 2016
Due to its low storage cost and fast query speed, cross-modal hashing (CMH) has been widely used for similarity search in multimedia retrieval applications.
Qing-Yuan Jiang, Wu-Jun Li
semanticscholar   +1 more source

Hopscotch Hashing

2008
We present a new class of resizable sequential and concurrent hash map algorithms directed at both uni-processor and multicore machines. The new hopscotch algorithms are based on a novel hopscotchmulti-phased probing and displacement technique that has the flavors of chaining, cuckoo hashing, and linear probing, all put together, yet avoids the ...
Herlihy M., Shavit N., Tzafrir M.
openaire   +1 more source

Multi-Task Consistency-Preserving Adversarial Hashing for Cross-Modal Retrieval

IEEE Transactions on Image Processing, 2020
Owing to the advantages of low storage cost and high query efficiency, cross-modal hashing has received increasing attention recently. As failing to bridge the inherent modality gap between modalities, most existing cross-modal hashing methods have ...
De Xie, Cheng Deng, Chao Li, Xianglong Liu, D. Tao   +4 more
semanticscholar   +1 more source

Adaptive Label Correlation Based Asymmetric Discrete Hashing for Cross-Modal Retrieval

IEEE Transactions on Knowledge and Data Engineering, 2021
Hashing methods have captured much attention for cross-modal retrieval in recent years. Most existing approaches mainly focus on preserving the semantic similarity across heterogeneous modalities in a shared Hamming subspace, while the label information ...
Huaxiong Li, Chao Zhang, Xiuyi Jia, Yang Gao, Chunlin Chen   +4 more
semanticscholar   +1 more source

Hashing practice: analysis of hashing and universal hashing

ACM SIGMOD Record, 1988
Much of the literature on hashing deals with overflow handling (collision resolution) techniques and its analysis. What does all the analytical results mean in practice and how can they be achieved with practical files? This paper considers the problem of achieving analytical performance of hashing techniques in practice with reference to successful ...
openaire   +1 more source

Deep Adversarial Discrete Hashing for Cross-Modal Retrieval

International Conference on Multimedia Retrieval, 2020
Cross-modal hashing has received widespread attentions on cross-modal retrieval task due to its superior retrieval efficiency and low storage cost. However, most existing cross-modal hashing methods learn binary codes directly from multimedia data, which
Cong Bai, Chao Zeng, Qing Ma, Jinglin Zhang, Shengyong Chen   +4 more
semanticscholar   +1 more source

Deep Collaborative Multi-View Hashing for Large-Scale Image Search

IEEE Transactions on Image Processing, 2020
Hashing could significantly accelerate large-scale image search by transforming the high-dimensional features into binary Hamming space, where efficient similarity search can be achieved with very fast Hamming distance computation and extremely low ...
Lei Zhu, Xu Lu, Zhiyong Cheng, Jingjing Li, Huaxiang Zhang   +4 more
semanticscholar   +1 more source

Dynamic hashing

BIT, 1978
A new file organisation called dynamic hashing is presented. The organisation is based on normal hashing, but the allocated storage space can easily be increased and decreased without reorganising the file, according to the number of records actually stored in the file.
openaire   +2 more sources

Scalable Deep Hashing for Large-Scale Social Image Retrieval

IEEE Transactions on Image Processing, 2020
Recent years have witnessed the wide application of hashing for large-scale image retrieval, because of its high computation efficiency and low storage cost. Particularly, benefiting from current advances in deep learning, supervised deep hashing methods
Hui Cui, Lei Zhu, Jingjing Li, Yang Yang, Liqiang Nie   +4 more
semanticscholar   +1 more source