Boosted random contextual semantic space based representation for visual recognition
Boosted random contextual semantic space based representation for visual recognition
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DOI：
10.1016/j.ins.2016.06.029
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发表时间：
2016-11
期刊：
影响因子：
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通讯作者：
Chunjie Zhang;Zhe Xue;Xiaobin Zhu;Huanian Wang;Qingming Huang;Q. Tian
中科院分区：
文献类型：
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作者：
Chunjie Zhang;Zhe Xue;Xiaobin Zhu;Huanian Wang;Qingming Huang;Q. Tian
视觉信息已被广泛用于图像表示。尽管事实证明非常有效，但视觉表示缺乏明确的语义。然而，如何为图像表示生成合适的语义空间仍然是一个有待解决的问题。为了对图像的视觉和语义表示进行联合建模，提出了一种基于增强型随机上下文语义空间的图像表示方法。首先使用局部特征的分布直方图来表示图像。通过随机选择训练图像来生成语义空间。然后，图像被相应地映射到语义空间。语义语境被用来对不同语义之间的相关性进行建模，然后用于分类。分类结果被用来以增强的方式对训练图像重新加权。利用加权后的图像构造新的分类语义空间。这样，我们就能够联合考虑图像的视觉和语义信息。在几个公开数据集上的图像分类实验表明了该方法的有效性。
Visual information has been widely used for image representation. Although proven very effective, the visual representation lacks explicit semantics. However, how to generate a proper semantic space for image representation is still an open problem that needs to be solved. To jointly model the visual and semantic representations of images, we propose a boosted random contextual semantic space based image representation method. Images are initially represented using local feature’s distribution histograms. The semantic space is generated by randomly selecting training images. Images are then mapped into the semantic space accordingly. Semantic context is explored to model the correlations of different semantics which is then used for classification. The classification results are used to re-weight training images in a boosted way. The re-weighted images are used to construct new semantic space for classification. In this way, we are able to jointly consider the visual and semantic information of images. Image classification experiments on several public datasets show the effectiveness of the proposed method.