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首页> 外文期刊>Journal of visual communication & image representation >Image automatic annotation via multi-view deep representation
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Image automatic annotation via multi-view deep representation

机译：通过多视图深度表示的图像自动注释

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The performance of text-based image retrieval is highly dependent on the tedious and inefficient manual work. For the purpose of realizing image keywords generated automatically, extensive work has been done in the area of image annotation. However, how to treat image diverse keywords and choose appropriate features are still two difficult problems. To address this challenge, we propose the multi-view stacked auto-encoder (MVSAE) framework to establish the correlations between the low-level visual features and high-level semantic information. In this paper, a new method, which incorporates the keyword frequencies and log-entropy, is presented to address the imbalanced distribution of keywords. In order to utilize the complementarities among diverse visual descriptors, we tactfully apply multi-view learning to search for the label-specific features. Thereafter, the image keywords are finally produced by appropriate features. Conducting extensive experiments on three popular data sets, we demonstrate that our proposed framework can achieve effective and favorable performance for image annotation. (C) 2015 Elsevier Inc. All rights reserved.
机译：基于文本的图像检索的性能高度依赖于繁琐且效率低下的手动工作。为了实现自动生成的图像关键词，在图像注释领域已经进行了广泛的工作。然而，如何对待图像多样化的关键词并选择合适的特征仍然是两个难题。为了解决这一挑战，我们提出了多视图堆叠自动编码器（MVSAE）框架，以建立低层视觉特征与高层语义信息之间的相关性。本文提出了一种结合关键字频率和对数熵的新方法来解决关键字分布不均的问题。为了利用各种视觉描述符之间的互补性，我们巧妙地应用多视图学习来搜索标签特定的功能。此后，图像关键字最终由适当的特征产生。在三个流行的数据集上进行了广泛的实验，我们证明了我们提出的框架可以实现有效和有利的图像标注性能。 （C）2015 Elsevier Inc.保留所有权利。

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