Extensible Max-Min Collaborative Retention for Online Mini-Batch Learning Hash Retrieval

Junpeng Tan, Xiaojun Yang, Zhijing Yang, Ruihan Chen, Yongyi Lu, Liang Lin

Abstract: Along with the concern of similarity measures in linear space, supervised online hash methods have been applied to the retrieval task. However, they ignored multi-dimensional space semantic mining and association characteristics will cause quantization errors of information hash code: 1) The similarity relation of discretized data needs to be considered in different spaces; 2) Latent semantic features need to be continuously embedded into hash code learning; 3) The correlation between the structure similarity and discrete hash matrices needs to be continuously optimized. To tackle these challenges, this paper proposes a novel Extensible Max-min Collaborative Retention Online Hash retrieval method based on mini-batch training data (EMCROH). It mainly includes the Max-min Bayesian Similarity Sparse Latent Hash module (MBSSLH), and the Repetition Collaborative Projection Learning module (RCPL). Specifically, MBSSLH is a max-min optimization model. Firstly, to explore the semantic similarity of multi-dimensional space, we propose a novel liner and nonlinear semantic similarity discrimination mechanism based on the log maximum likelihood similarity estimation with Euclidean space and minimize the input batch data features with a common projection matrix. Moreover, to further mine the potential semantic information of the discretization, we also propose a robust sparse discrete latent semantic information extraction submodule based on double latent factors. RCPL can extend the data externally using the repetition collaborative projection matrix with robustness regularization constraint. Finally, a novel max-min embedding iterative step is proposed to solve the batch discrete optimization problem based on Augmented Lagrange Multipliers (ALM) with Alternating Direction Minimization (ADM). Extensive experiments on several well-known large databases demonstrate that EMCROH outperforms the state-of-the-art hash methods.