Cluster-wise Unsupervised Hashing For Cross-modal Similarity Search
2019 · Lu Wang, Jie Yang
Abstract
Large-scale cross-modal hashing similarity retrieval has attracted more and more attention in modern search applications such as search engines and autopilot, showing great superiority in computation and storage. However, current unsupervised cross-modal hashing methods still have some limitations: (1)many methods relax the discrete constraints to solve the optimization objective which may significantly degrade the retrieval performance;(2)most existing hashing model project heterogenous data into a common latent space, which may always lose sight of diversity in heterogenous data;(3)transforming real-valued data point to binary codes always results in abundant loss of information, producing the suboptimal continuous latent space. To overcome above problems, in this paper, a novel Cluster-wise Unsupervised Hashing (CUH) method is proposed. Specifically, CUH jointly performs the multi-view clustering that projects the original data points from different modalities into its own low-dimen
Authors
(none)
Tags
Stats
Related papers
- Discriminative Supervised Hashing For Cross-modal Similarity Search (2018)7.81
- Unsupervised Multi-modal Hashing For Cross-modal Retrieval (2019)8.35
- Efficient Discrete Supervised Hashing For Large-scale Cross-modal Retrieval (2019)11.08
- Comprehensive Graph-conditional Similarity Preserving Network For Unsupervised Cross-modal Hashing (2020)3.14
- Weakly-paired Cross-modal Hashing (2019)0.00
- Adaptive Structural Similarity Preserving For Unsupervised Cross Modal Hashing (2022)10.97
- Asymmetric Correlation Quantization Hashing For Cross-modal Retrieval (2020)10.74
- Fusion-supervised Deep Cross-modal Hashing (2019)8.60