CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
ActiveAd: A novel framework of linking ad videos to online products
Wang, Jinqiao1; Xu, Min2; Lu, Hanqing1; Burnett, Ian2
Source PublicationNEUROCOMPUTING
AbstractWith the wide use of consumer electronics and the rapid development of online shopping, more and more ad videos are developed for IDTV and mobile users. However, a huge amount of time spending on the Internet advertising somehow brings users uncomfortable viewing experience rather than effectively generates high consumption of advertised products. Therefore, it is urgent to find a viewer-friendly and advertiser-beneficial solution to launch ads. This paper is the first attempt to improve the effectiveness of advertising through combining online shopping information with an ad video and directing viewers to proper online shopping places. The proposed ActiveAd framework includes four main components. Firstly, an ad video analysis component detects both syntactic and semantic elements from ad videos, e.g. FMPIs (Frame Marked with Production Information), visual concepts, and textual keywords within the ad videos. Our ad video analysis provides a comprehensive solution to extract meaningful elements from ad videos. Secondly, a visual linking by search component is proposed to collect websites which contain similar images as FMPIs. Features used for the visual search are weighted and fused in order to ensure the uniformity of search results. Thirdly, different kinds of tags and product categories extracted from collected websites are aggregated in order to identify the representative text of the product. Finally, query keywords are selected through calculating cosine similarity from two kinds of keywords, i.e. keywords identified from tag aggregation and keywords obtained through ad video analysis (visual concept detection and textual keyword detection). A query vector is generated by selected keywords and used to retrieve product online. In this paper, a powerful cross-media contextual search including visual search, tag aggregation and textual search is achieved with the help of ad video analysis. Experiments demonstrate that our proposed ActiveAd achieves product recommendation effectively and efficiently. (C) 2016 Elsevier B.V. All rights reserved.
KeywordAd Video Analysis Visual Search Tag Aggregation Textual Search
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
Funding Organization863 Program(2014AA015104) ; National Natural Science
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000374363900010
Citation statistics
Cited Times:6[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100864, Peoples R China
2.Univ Technol Sydney, Fac Engn & IT, Sydney, NSW 2007, Australia
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Wang, Jinqiao,Xu, Min,Lu, Hanqing,et al. ActiveAd: A novel framework of linking ad videos to online products[J]. NEUROCOMPUTING,2016(185):82-92.
APA Wang, Jinqiao,Xu, Min,Lu, Hanqing,&Burnett, Ian.(2016).ActiveAd: A novel framework of linking ad videos to online products.NEUROCOMPUTING(185),82-92.
MLA Wang, Jinqiao,et al."ActiveAd: A novel framework of linking ad videos to online products".NEUROCOMPUTING .185(2016):82-92.
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