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When VLAD Met Hilbert

机译:当vlad遇见希尔伯特时

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摘要

In many challenging visual recognition tasks where training data is limited, Vectors of Locally Aggregated Descriptors (VLAD) have emerged as powerful image/video representations that compete with or outperform state-of the-art approaches. In this paper, we address two fundamental limitations of VLAD: its requirement for the local descriptors to have vector form and its restriction to linear classifiers due to its high-dimensionality. To this end, we introduce a kernelized version of VLAD. This not only lets us inherently exploit more sophisticated classification schemes, but also enables us to efficiently aggregate nonvector descriptors (e.g., manifold-valued data) in the VLAD framework. Furthermore, we propose an approximate formulation that allows us to accelerate the coding process while still benefiting from the properties of kernel VLAD. Our experiments demonstrate the effectiveness of our approach at handling manifold-valued data, such as covariance descriptors, on several classification tasks. Our results also evidence the benefits of our nonlinear VLAD descriptors against the linear ones in Euclidean space using several standard benchmark datasets.
机译:在许多具有挑战性的视觉识别任务中,其中训练数据有限,局部聚合描述符(VLAD)的向量出现为与最先进的方法竞争或倾斜的方法的强大图像/视频表示。在本文中,我们解决了VLAD的两个基本局限性:由于其高维度,其对局部描述符的要求及其对线性分类器的限制。为此,我们介绍了VLAD的内核版本。这不仅让我们本质地利用更复杂的分类方案,而且还使我们能够在VLAD框架中有效地聚合非传道描述仪(例如,歧管值数据)。此外,我们提出了一种近似的配方,其允许我们加速编码过程,同时仍然受益于核Vlad的性质。我们的实验证明了我们在几种分类任务上处理歧管价值数据(如协方差描述符)的方法的有效性。我们的结果还证明了我们的非线性VLAD描述符对使用多个标准基准数据集的欧几里德空间中线性符号的优势。

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