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首页> 外文会议>SIBGRAPI Conference on Graphics, Patterns and Images >Unsupervised Similarity Learning through Cartesian Product of Ranking References for Image Retrieval Tasks
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Unsupervised Similarity Learning through Cartesian Product of Ranking References for Image Retrieval Tasks

机译：通过图像检索任务的排名参考的笛卡尔乘积进行无监督相似性学习

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摘要

Despite the consistent advances in visual features and other Content-Based Image Retrieval techniques, measuring the similarity among images is still a challenging task for effective image 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, four public datasets and several image 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基于内容的图像检索技术取得了一致的进步，但测量图像之间的相似性对于有效的图像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。考虑到各个方面，四个公共数据集和几个图像特征，进行了广泛的实验评估。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。所提出的方法取得了显着的有效性提高，包括在流行基准上具有竞争性的最新技术成果。

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