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Cross-media retrieval with collective deep semantic learning

机译：跨媒体检索与集体深度语义学习

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摘要

Cross-media retrieval is becoming a new trend of information retrieval technique. It has been received great attentions from both academia and industry. In this paper, we propose an effective retrieval method, dubbed as Cross-media Retrieval with Collective Deep Semantic Learning (CR-CDSL), to solve the problem. Two complementary deep neural networks are first learned to collectively project image and text samples into a joint semantic representation. Based on it, weak semantic labels are then generated accordingly for unlabeled images and texts. They are exploited further with the pre-labeled training samples to retrain the retrieval model, which can discover a discriminative shared semantic space for achieving cross-media retrieval. Specifically, Deep Restricted Boltzmann Machines (DRBM) is employed to initialize the weights of two deep neural networks. With the weak labels generated from collective deep semantic learning, the discriminative capability of retrieval model can be enhanced. Thus, the retrieval performance of the model could be improved. Experiments are evaluated on several publicly available cross-media datasets. The obtained experimental results demonstrate the superior performance of the proposed approach compared with several state-of-the-art techniques.
机译：跨媒体检索正成为信息检索技术的新趋势。它已经引起了学术界和工业界的极大关注。在本文中，我们提出了一种有效的检索方法，称为集体深度语义学习跨媒体检索（CR-CDSL），以解决该问题。首先学习了两个互补的深度神经网络，以将图像和文本样本共同投影为联合语义表示。基于此，然后针对未标记的图像和文本生成弱语义标签。它们与预先标记的训练样本一起被进一步利用以重新训练检索模型，该模型可以发现区分性共享语义空间以实现跨媒体检索。具体来说，采用深度受限玻尔兹曼机（DRBM）来初始化两个深度神经网络的权重。利用集体深度语义学习产生的弱标签，可以增强检索模型的判别能力。因此，可以提高模型的检索性能。在几个公开的跨媒体数据集上评估了实验。与几种最新技术相比，所获得的实验结果证明了该方法的优越性能。

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  • 来源
    《Multimedia Tools and Applications》 |2018年第17期|22247-22266|共20页
  • 作者

    Bin Zhang; Lei Zhu; Jiande Sun; Huaxiang Zhang;

  • 作者单位

    School of Information Science and Engineering, Shandong Normal University,Institute of Data Science and Technology, Shandong Normal University;

    School of Information Science and Engineering, Shandong Normal University,Institute of Data Science and Technology, Shandong Normal University;

    School of Information Science and Engineering, Shandong Normal University,Institute of Data Science and Technology, Shandong Normal University;

    School of Information Science and Engineering, Shandong Normal University,Institute of Data Science and Technology, Shandong Normal University;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Cross-media retrieval; Collective deep semantic learning; Deep neural network; Deep restricted boltzmann machines;

    机译：跨媒体检索;集体深度语义学习;深度神经网络;深度受限的boltzmann机器;

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