Classifying advertising video by topicalizing high-level semantic concepts
Classifying advertising video by topicalizing high-level semantic concepts
复制标题
通过主题化高级语义概念对广告视频进行分类
DOI：
10.1007/s11042-018-5801-3
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发表时间：
2018-10-01
影响因子：
3.6
通讯作者：
Zheng, Yuanjie
中科院分区：
文献类型：
--
作者：
Hou, Sujuan;Zhou, Shangbo;Zheng, Yuanjie
最近视频的激增推动了研究的各种应用，从视频分析到索引和检索。这些应用程序极大地受益于视频的领域知识。广告视频作为一种特殊的视频类型，其分类是一项关键任务，因为它可以根据类别或类型自动组织视频，从而进一步实现广告视频的索引和检索。然而，由于广告视频内容不受约束、表达方式独特，分类难度较大。虽然许多研究都关注于选择与目标视频相关的广告，但据我们所知，很少有研究关注广告视频的分类。为了对广告视频进行分类，我们提出了一种新的视频表示，旨在以无监督的方式捕获广告视频的潜在语义。特别地，本文将品牌/标志信息与高级对象信息之间的后验发生概率整合到一个潜在的Dirichlet分配统一学习范式中，命名为ppLDA。该方法获得了广告视频的主题表示，可以支持类别相关任务。我们对从互联网下载的10111个真实广告视频进行了实验，结果表明该方法可以有效地区分广告视频。
The recent proliferation of videos has driven the research into various applications, ranging from video analysis to indexing and retrieval. These applications greatly benefit from domain knowledge of videos. As a special kind of videos, classifying ad video is a key task because it allows automatic organization of videos according to categories or genres, and this further enables ad video indexing and retrieval. However, classifying ad video is challenging due to its unconstraint content and distinctive expression. While many studies focus on selecting ads relevant to the target videos, to the best of our knowledge, few focuses on ad video classification. To classify ad video, we propose a novel video representation that aims to capture the latent semantics of ad video in an unsupervised manner. In particular, this paper integrates the posterior occurrence probability between brand/logo information and the high-level object information into a latent Dirichlet allocation unified learning paradigm, named ppLDA. A topical representation for ad video is obtained by the proposed method, which can support category-related task. Our experiments on 10,111 real-world ad videos downloaded from Internet demonstrate that the proposed method could effectively differentiate ad videos.