学习异构数据的分层网络视频分类

Xianming Liu, H. Yao, R. Ji, Pengfei Xu, Xiaoshuai Sun, Q. Tian
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引用次数: 7

摘要

YouTube等网络视频由于本体复杂,难以获得足够精确标记的训练数据并进行分析。为了解决这些问题,我们通过学习异构网络数据,提出了一个分层的网络视频分类框架,并通过学习元数据构建了一个自下而上的视频概念语义森林。主要贡献有两方面:首先,基于网络社区数据,分析了中层概念的分布,提出了概念再分布假设,构建了有效的迁移学习算法;此外,提出了一种类似adaboost的迁移学习算法,将从Flickr图像中学习到的知识转移到YouTube视频域,从而便于视频分类。其次,从YouTube和Flickr标签中挖掘出一组语义森林(Semantic Forest)的分层分类法,这些分类法在语义层面上更好地反映了用户的意图。利用语义森林构造自底向上的语义集成,以新颖的视角对视频内容进行分层分析。对从Flickr和YouTube上收集的数据集进行了一组实验。与最先进的框架相比,该框架具有更强的鲁棒性和对网络噪声的容忍度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Learning heterogeneous data for hierarchical web video classification
Web videos such as YouTube are hard to obtain sufficient precisely labeled training data and analyze due to the complex ontology. To deal with these problems, we present a hierarchical web video classification framework by learning heterogeneous web data, and construct a bottom-up semantic forest of video concepts by learning from meta-data. The main contributions are two-folds: firstly, analysis about middle-level concepts' distribution is taken based on data collected from web communities, and a concepts redistribution assumption is made to build effective transfer learning algorithm. Furthermore, an AdaBoost-Like transfer learning algorithm is proposed to transfer the knowledge learned from Flickr images to YouTube video domain and thus it facilitates video classification. Secondly, a group of hierarchical taxonomies named Semantic Forest are mined from YouTube and Flickr tags which reflect better user intention on the semantic level. A bottom-up semantic integration is also constructed with the help of semantic forest, in order to analyze video content hierarchically in a novel perspective. A group of experiments are performed on the dataset collected from Flickr and YouTube. Compared with state-of-the-arts, the proposed framework is more robust and tolerant to web noise.
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