Cluster-wise unsupervised hashing for cross-modal similarity search
作者：
Highlights：
• Proposing Cluster-wise Unsupervised Hashing to solve effective and efficient large-scale retrieval across modalities.
• Utilizing the multi-view clustering to project the multi-modal data into its low-dimensional space, saving the diversity.
• The proposed cluster-wise prototype making different data points in the same cluster having the same binary codes.
• No much loss of information during transforming the real-valued data into binary codes based on the cluster-wise prototype.
• Designing a discrete optimization framework to directly learn the unified binary codes for heterogeneous modalities.
摘要
•Proposing Cluster-wise Unsupervised Hashing to solve effective and efficient large-scale retrieval across modalities.•Utilizing the multi-view clustering to project the multi-modal data into its low-dimensional space, saving the diversity.•The proposed cluster-wise prototype making different data points in the same cluster having the same binary codes.•No much loss of information during transforming the real-valued data into binary codes based on the cluster-wise prototype.•Designing a discrete optimization framework to directly learn the unified binary codes for heterogeneous modalities.
论文关键词：Cross-modal similarity retrieval,Multi-view clustering,The cluster-wise code-prototypes,Cross-modal hashing,
论文评审过程：Received 30 December 2019, Revised 10 August 2020, Accepted 29 October 2020, Available online 29 October 2020, Version of Record 5 November 2020.
论文官网地址：https://doi.org/10.1016/j.patcog.2020.107732
