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首页> 外文期刊>Journal of visual communication & image representation >Multimedia annotation via semi-supervised shared-subspace feature selection
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Multimedia annotation via semi-supervised shared-subspace feature selection

机译：通过半监督共享子空间特征选择进行多媒体注释

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摘要

With the rapid development of social network and computer technologies, we always confront with high dimensional multimedia data. It is time-consuming and unrealistic to organize such a large amount of data. Most existing methods are not appropriate for large-scale data due to their dependence of Laplacian matrix on training data. Normally, a given multimedia sample is usually associated with multiple labels, which are inherently correlated to each other. Although traditional methods could solve this problem by translating it into several single-label problems, they ignore the correlation among different labels. In this paper, we propose a novel semi-supervised feature selection method and apply it to the multimedia annotation. Both labeled and unlabeled samples are sufficiently utilized without the need of graph construction, and the shared information between multiple labels is simultaneously uncovered. We apply the proposed algorithm to both web page and image annotation. Experimental results demonstrate the effectiveness of our method. (C) 2017 Elsevier Inc. All rights reserved.
机译：随着社交网络和计算机技术的迅速发展，我们始终面对高维多媒体数据。组织如此大量的数据既费时又不切实际。大多数现有方法由于它们的拉普拉斯矩阵依赖于训练数据而不适用于大规模数据。通常，给定的多媒体样本通常与多个标签相关联，这些标签固有地相互关联。尽管传统方法可以通过将其转化为几个单标签问题来解决此问题，但它们忽略了不同标签之间的相关性。本文提出了一种新颖的半监督特征选择方法，并将其应用于多媒体标注。标记和未标记的样本都可以得到充分利用，而无需构建图，并且可以同时发现多个标记之间的共享信息。我们将提出的算法应用于网页和图像标注。实验结果证明了我们方法的有效性。 （C）2017 Elsevier Inc.保留所有权利。

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