Gradient-based Signatures for Efficient Similarity Search in Large-scale Multimedia Databases
Gradient-based Signatures for Efficient Similarity Search in Large-scale Multimedia Databases
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DOI：
10.1145/2806416.2806459
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发表时间：
2015-10
期刊：
影响因子：
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通讯作者：
C. Beecks;M. S. Uysal;Judith Hermanns;T. Seidl
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文献类型：
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作者：
C. Beecks;M. S. Uysal;Judith Hermanns;T. Seidl
随着多媒体技术的不断发展，如何高效地访问大规模多媒体数据库成为一个重要的问题。给定一个包含数百万个多媒体对象的多媒体数据库，如何近似相应特征表示的基于内容的属性，以便高效且高准确度地进行相似性搜索？在本文中，我们提出了基于梯度的签名的概念，以便通过生成模型聚合多媒体对象的基于内容的特征。我们提供了理论见解，我们的方法，包括封闭形式的表达式计算基于梯度的签名相对于高斯混合模型，并另外调查不同的二进制化方法，基于梯度的签名，以查询数据库，包括数百万的多媒体对象，在不到一秒的高精度。
With the continuous rise of multimedia, the question of how to access large-scale multimedia databases efficiently has become of crucial importance. Given a multimedia database comprising millions of multimedia objects, how to approximate the content-based properties of the corresponding feature representations in order to carry out similarity search efficiently and with high accuracy? In this paper, we propose the concept of gradient-based signatures in order to aggregate content-based features of multimedia objects by means of generative models. We provide theoretical insights into our approach including closed-form expressions for the computation of gradient-based signatures with respect to Gaussian mixture models and additionally investigate different binarization methods for gradient-based signatures in order to query databases comprising millions of multimedia objects with high accuracy in less than one second.