Video fingerprinting using Latent Dirichlet Allocation and facial images

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• 作者：Nicholas Vretos ; ^{vretos@aiia.csd.auth.gr}  ; [Author Vitae] ; Nikos Nikolaidis ^{nikolaid@aiia.csd.auth.gr}  ; [Author Vitae] ; Ioannis Pitas ^{pitas@aiia.csd.auth.gr}  ; [Author Vitae]
• 关键词：Latent Dirichlet Allocation ; Video fingerprinting ; Perceptual hashing
• 刊名：Pattern Recognition
• 出版年：2012
• 出版时间：July, 2012
• 年：2012
• 卷：45
• 期：7
• 页码：2489-2498
• 全文大小：745 K

文摘

This paper investigates the possibility of extracting latent aspects of a video in order to develop a video fingerprinting framework. Semantic visual information about humans, more specifically face occurrences in video frames, along with a generative probabilistic model, namely the Latent Dirichlet Allocation (LDA), are used for this purpose. The latent variables, namely the video topics are modeled as a mixture of distributions of faces in each video. The method also involves a clustering approach based on Scale Invariant Features Transform (SIFT) for clustering the detected faces and adapts the bag-of-words concept into a bag-of-faces one, in order to ensure exchangeability between topics distributions. Experimental results, on three different data sets, provide low misclassification rates of the order of 2 % and false rejection rates of 0 % . These rates provide evidence that the proposed method performs very efficiently for video fingerprinting.