Robust and discrete matrix factorization hashing for cross-modal retrieval

作者：

Highlights：

• We present a novel two-step hashing algorithm (RDMH) for cross-media retrieval.

• We develop a discrete matrix factorization scheme, the binary codes can be learned directly. The quantization errors caused by the rounding or relaxation scheme can be avoided.

• We utilize l2,1-norm in the proposed framework to enhance the robustness of our method, which makes the proposed method robust to noises and outliers.

• We propose a novel autoencoder strategy to learn the hash functions. More valuable information can be preserved, making the hash functions more powerful.

摘要

•We present a novel two-step hashing algorithm (RDMH) for cross-media retrieval.•We develop a discrete matrix factorization scheme, the binary codes can be learned directly. The quantization errors caused by the rounding or relaxation scheme can be avoided.•We utilize l2,1-norm in the proposed framework to enhance the robustness of our method, which makes the proposed method robust to noises and outliers.•We propose a novel autoencoder strategy to learn the hash functions. More valuable information can be preserved, making the hash functions more powerful.

论文关键词：Cross-modal retrieval,Hashing,Autoencoder,Discrete optimization,

论文评审过程：Received 22 January 2020, Revised 23 July 2021, Accepted 20 September 2021, Available online 22 September 2021, Version of Record 7 October 2021.

论文官网地址：https://doi.org/10.1016/j.patcog.2021.108343