从大规模博客圈中挖掘突出图像

Xian Chen, Meilian Chen, Hyoseop Shin, Eun Yi Kim
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引用次数: 1

摘要

用户生成的图片现在在社交媒体平台上很流行,比如Facebook、Twitter和各种博客圈。这些图像可以根据它们的相关主题进行分类和排名。在本文中,我们提出并比较了从博客圈的大量图像中挖掘与特定主题或对象相关的显著图像的候选方案。识别显著图像包括几个步骤:计算图像之间的相似性,k-means聚类图像和对图像进行排序。在每个步骤中,我们提出了一组备选方案,并通过对每个方案的性能进行实证比较,提出了一个最优的组合方案。此外,为了解决可扩展性问题,我们还提供了一个分布式版本的方案和基于MapReduce的Hadoop环境的实验结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mining salient images from a large-scale blogosphere
User-generated images are now prevalent across social media platforms, such as Facebook, Twitter, and various blogospheres. These images can be categorized and ranked based on their relevant topics. In this paper, we present and compare candidate schemes for mining salient images related to a specific topic or object among a large number of images from a blogosphere. Identifying salient images consists of several steps: calculating the similarity between images, k-means clustering images, and ranking images. In each step, we propose a set of alternatives and as a result, present an optimal combination scheme by conducting an empirical comparison of the performance of each scheme. Furthermore, to address scalability, we also present a distributed version of the schemes and experimental results based on MapReduce on top of a Hadoop environment.
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