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Multi-layer multi-view topic model for classifying advertising video.pdf

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Multi-layer multi-view topic model for classifying advertising video.pdf

Pattern Recognition 68 (2017) 66–81 Contents lists available at ScienceDirect Pattern Recognition journal homepage: /locate/patcog Multi-layer multi-view topic model for classifying advertising video Sujuan Hou a,e, Ling Chen b, Dacheng Tao b, Shangbo Zhou c, Wenjie Liu d, Yuanjie Zheng a,e,f,? a School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China b Centre for Quantum Computation and Intelligent Systems, FEIT,University of Technology, Sydney, Australia c College of Computer Science, Chongqing University, Chongqing 400030, China d School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, PR China e Institute of Life Sciences, Shandong Normal University, Jinan 250014, China f Key Laboratory of Intelligent Information Processing, Shandong Normal University, Jinan 250014, China article info Article history: Received 9 August 2016 Revised 30 November 2016 Accepted 1 March 2017 Available online 2 March 2017 Keywords: Video representation Ad video classi?cation Multi-layer Multi-view Topic model abstract The recent proliferation of advertising (ad) videos has driven the research in multiple applications, ranging from video analysis to video indexing and retrieval. Among them, 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 compared to other types of video classi?cation because of its unconstrained content. While many studies focus on embedding ads relevant to videos, to our knowledge, few focus on ad video classi?cation. In order to classify ad video, this paper proposes a novel ad video representation that aims to su?ciently capture the latent semantics of video content from multiple views in an unsupervised manner. In particular, we represent ad videos from four views, including bag-of-feature (BOF), vector of loca