Feature set aggregator: unsupervised representation learning of sets for their comparison
Feature set aggregator: unsupervised representation learning of sets for their comparison
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DOI：
10.1007/s11042-019-08078-y
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发表时间：
2019-08
影响因子：
3.6
通讯作者：
T. Furuya;Ryutarou Ohbuchi
中科院分区：
文献类型：
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作者：
T. Furuya;Ryutarou Ohbuchi
无标签多媒体数据的无监督表示学习是其索引、聚类和检索的重要而又具有挑战性的问题。已经有很多尝试从一组未标记的2D图像中学习表示。然而，相比之下，通常用于描述多媒体数据的高维特征向量无序集的无监督表示学习却很少受到关注。其中一个例子是描述二维图像的一组局部视觉特征。本文提出了一种新的特征集聚合器（Feature Set Aggregator， FSA）算法，用于对高维特征集进行准确、高效的比较。FSA通过优化使用两个训练目标的组合来学习无序特征集的表示或嵌入，这两个训练目标是集重建和集嵌入，精心设计用于集对集的比较。在三维形状、二维图像和文本文档三种多媒体信息检索场景下的实验评估表明了该算法的有效性和通用性。
Unsupervised representation learning of unlabeled multimedia data is important yet challenging problem for their indexing, clustering, and retrieval. There have been many attempts to learn representation from a collection of unlabeled 2D images. In contrast, however, less attention has been paid to unsupervised representation learning forunordered sets of high-dimensional feature vectors, which are often used to describe multimedia data. One such example is set of local visual features to describe a 2D image. This paper proposes a novel algorithm called Feature Set Aggregator (FSA) for accurate and efficient comparison among sets of high-dimensional features. FSA learns representation, or embedding, of unordered feature sets via optimization using a combination of two training objectives, that are, set reconstruction and set embedding, carefully designed for set-to-set comparison. Experimental evaluation under three multimedia information retrieval scenarios using 3D shapes, 2D images, and text documents demonstrates efficacy as well as generality of the proposed algorithm.