Flexible Online Multi-modal Hashing for Large-scale Multimedia Retrieval

计算机科学 散列函数 判别式 二进制代码 特征哈希 双重哈希 动态完美哈希 局部敏感散列 情态动词 人工智能 机器学习 数据挖掘 哈希表 二进制数 化学 计算机安全 高分子化学 算术 数学
作者
Xu Lu,Lei Zhu,Zhiyong Cheng,Jingjing Li,Xiushan Nie,Huaxiang Zhang
标识
DOI:10.1145/3343031.3350999
摘要

Multi-modal hashing fuses multi-modal features at both offline training and online query stage for compact binary hash learning. It has aroused extensive attention in research filed of efficient large-scale multimedia retrieval. However, existing methods adopt batch-based learning scheme or unsupervised learning paradigm. They cannot efficiently handle the very common online streaming multi-modal data (for batch-learning methods), or learn the hash codes suffering from limited discriminative capability and less flexibility for varied streaming data (for existing online multi-modal hashing methods). In this paper, we develop a supervised Flexible Online Multi-modal Hashing (FOMH) method to adaptively fuse heterogeneous modalities and flexibly learn the discriminative hash code for the newly coming data, even if part of the modalities is missing. Specifically, instead of adopting the fixed weights, the modalities weights in FOMH are automatically learned with the proposed flexible multi-modal binary projection to timely capture the variations of streaming samples. Further, we design an efficient asymmetric online supervised hashing strategy to enhance the discriminative capability of the hash codes, while avoiding the challenging symmetric semantic matrix decomposition and storage cost. Moreover, to support fast hash updating and avoid the propagation of binary quantization errors in online learning process, we propose to directly update the hash codes with an efficient discrete online optimization. Experiments on several public multimedia retrieval datasets validate the superiority of the proposed method from various aspects.
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