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Subspace-level dictionary fusion for robust multimedia classification

机译：子空间级词典融合，用于强大的多媒体分类

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摘要

Nowadays, dictionary learning has become an important tool in many classification tasks, especially for images. The tailor-made atoms in a dictionary are trained for the reconstruction of the test sample. In the classification, atoms are associated with different classes from several subspaces such that the test sample is labeled according to the distances of each subspace. However, it is hard to fix the number of atoms to obtain the optimal result for each scenario since the optimal subspaces required are different. To improve the classification performance as well as the robustness, we proposed subspace-level dictionary fusion (SLDF) to construct a dictionary-based classifier. A full-size dictionary and a locality-constrained dictionary are constructed in parallel. Then, the reconstruction coefficients of the two dictionaries are obtained, which leads to a pair of distances between the test sample and the subspaces. Finally, a decision is made according to the pair-wise fusion of the distances. The experimental results on multimedia datasets from distinct categories such as image, text, and audio show that the proposed method outperforms other state-of-the-art dictionary-based classification methods with accuracies of 99.74% (image), 83.96% (Text), and 87.07% (Audio).
机译：如今，字典学习已成为许多分类任务中的重要工具，尤其是图像。字典中的定制原子培训用于测试样品的重建。在分类中，原子与来自多个子空间的不同类相关联，使得根据每个子空间的距离标记测试样品。然而，由于所需的最佳子空间是不同的，因此难以修复每个场景的最佳结果以获得最佳结果。为了提高分类性能以及稳健性，我们提出了子空间级字典融合（SLDF）来构建基于词典的分类器。全尺寸字典和地区约束词典并行构建。然后，获得两个词典的重建系数，这导致测试样品和子空间之间的一对距离。最后，根据距离的一对融合来进行决定。来自图像，文本和音频等不同类别的多媒体数据集的实验结果表明，所提出的方法优于其他基于最先进的字典的分类方法，精度为99.74％（图像），83.96％（文本） ，87.07％（音频）。

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