Deep forest hashing for image retrieval

作者：

Highlights：

• The proposed method considers three types of similarity metrics to preserve the semantic similarity and manifold similarity among the data points in the Hamming space.

• Different sized sliding windows are used to extract multi-grained features from raw data. And the feature extraction phase is dependent on the hash function learning stage, which helps in learning better hash functions.

• Compared with deep neural network-based hashing methods, the proposed method has fewer hyperparameters, faster training speed and easier theoretical analysis.

• The proposed method learns shorter binary code representations to achieve effective and efficient image retrieval.

摘要

•The proposed method considers three types of similarity metrics to preserve the semantic similarity and manifold similarity among the data points in the Hamming space.•Different sized sliding windows are used to extract multi-grained features from raw data. And the feature extraction phase is dependent on the hash function learning stage, which helps in learning better hash functions.•Compared with deep neural network-based hashing methods, the proposed method has fewer hyperparameters, faster training speed and easier theoretical analysis.•The proposed method learns shorter binary code representations to achieve effective and efficient image retrieval.

论文关键词：Hashing learning,Image retrieval,Deep forest hashing,Shorter binary codes

论文评审过程：Received 5 December 2018, Revised 21 May 2019, Accepted 5 June 2019, Available online 5 June 2019, Version of Record 13 June 2019.

论文官网地址：https://doi.org/10.1016/j.patcog.2019.06.005