联合哈希特征和分类器学习的跨模态检索算法

Joint Hashing Feature and Classifier Learning for Cross-Modal Retrieval

为了解决跨模态检索算法检索准确率较低、训练时间较长等问题,文中提出联合哈希特征和分类器学习的跨模态检索算法(HFCL).采用统一的哈希码描述语义相同的不同模态数据.在训练阶段,利用标签信息学习具有鉴别性的哈希码.第二阶段基于生成的鉴别性哈希码,采用核逻辑回归学习各模态的哈希函数.在测试阶段,给定任意一个模态查询样本,利用学习的哈希函数生成哈希特征,从数据库中检索与之语义相关的另一模态数据.在3个公开数据集上的实验验证HFCL的有效性.

To solve the problem of low retrieval accuracy and long training time in cross-modal retrieval algorithms,a cross-modal retrieval algorithm joining hashing feature and classifier learning(HFCL)is proposed.Uniform hash codes are utilized to describe different modal data with the same semantics.In the training stage,label information is utilized to study discriminative hash codes.And the kernel logistic regression is adopted to learn the hash function of each modal.In the testing stage,for any sample,the hash feature is generated by learned hash function,and another modal datum related to its semantics is retrieved from the database.Experiments on three public datasets verify the effectiveness of HFCL.