Title
Efficient Semi-Supervised Multimodal Hashing With Importance Differentiation Regression
Abstract
Multi-modal hashing learns compact binary hash codes by collaborating heterogeneous multi-modal features at both the model training and online retrieval stages to support large-scale multimedia retrieval. Previous multi-modal hashing methods mainly focus on supervised and unsupervised hashing. The performance of supervised hashing largely relies on the number of labeled data, which is practically expensive to obtain. Unsupervised hashing methods cannot effectively capture the semantic correlations of multi-modal data without any labels for supervision. In this paper, we propose an Efficient Semi-supervised Multi-modal Hashing with Importance Differentiation Regression (ESMH-IDR) model, which can alleviate the existing problems by learning from both labeled and unlabeled data. Specifically, in this paper, we develop an efficient semi-supervised multi-modal hash code learning module. It learns the hash codes for labeled data in an efficient asymmetric way, and simultaneously performs nonlinear regression using the same projection matrix as the labeled samples to preserve the intrinsic data structure of unlabeled data. Besides, different from existing methods, we propose an importance differentiation regression strategy to learn hash functions by specially considering the different importance of hash codes learned from the labeled and unlabeled samples. Finally, we develop an efficient discrete optimization method guaranteed with convergence to iteratively solve the hash optimization problem. Experiments on several public multimedia retrieval datasets demonstrate the superiority of our proposed method on both retrieval effectiveness and efficiency. Our source codes and testing datasets can be obtained at https://github.com/ChaoqunZheng/ESMH.
Year
DOI
Venue
2022
10.1109/TIP.2022.3203216
IEEE TRANSACTIONS ON IMAGE PROCESSING
Keywords
DocType
Volume
Codes, Semantics, Hash functions, Training, Feature extraction, Kernel, Data models, Multi-modal hashing, semi-supervised hashing, hash function learning, importance differentiation regression
Journal
31
Issue
ISSN
Citations 
1
1057-7149
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Chaoqun Zheng1111.47
Lei Zhu285451.69
Zheng Zhang354940.45
Jingjing Li459744.26
Xiaomei Yu500.34