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Unsupervised similarity learning through Cartesian product of ranking references

机译：通过排名参考的笛卡尔积进行无监督的相似性学习

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摘要

Despite the consistent advances in visual features and other Multimedia Information Retrieval (MIR) techniques, measuring the similarity among multimedia objects is still a challenging task for an effective retrieval. In this scenario, similarity learning approaches capable of improving the effectiveness of retrieval in an unsupervised way are indispensable. A novel method, called Cartesian Product of Ranking References (CPRR), is proposed with this objective in this paper. The proposed method uses Cartesian product operations based on rank information for exploiting the underlying structure of datasets. Only subsets of ranked lists are required, demanding low computational efforts. An extensive experimental evaluation was conducted considering various aspects, seven public multimedia datasets (images and videos) and several different features. Besides effectiveness, experiments were also conducted to assess the efficiency of the method, considering parallel and heterogeneous computing on CPU and GPU devices. The proposed method achieved significant effectiveness gains, including competitive state-of-the-art results on popular benchmarks.
机译：尽管视觉功能和其他多媒体信息检索（MIR）技术取得了一致的进步，但测量多媒体对象之间的相似性对于有效检索仍然是一项艰巨的任务。在这种情况下，能够以无监督方式提高检索效率的相似性学习方法是必不可少的。为此目的，提出了一种新的方法，称为笛卡尔参考引用乘积（CPRR）。所提出的方法使用基于等级信息的笛卡尔乘积运算来利用数据集的基础结构。只需要排名列表的子集，所需的计算量就很少。进行了广泛的实验评估，考虑了各个方面，七个公共多媒体数据集（图像和视频）以及几个不同的功能。除了有效性之外，还考虑了CPU和GPU设备上的并行和异构计算，还进行了实验以评估该方法的效率。所提出的方法获得了显着的有效性提升，包括在流行基准上具有竞争性的最新技术成果。

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