کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
530693 | 869784 | 2012 | 10 صفحه PDF | دانلود رایگان |

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.
► Latent Dirichlet Allocation for Perceptual Hashing.
► Scale Invariant Features Transform (SIFT) Based Face Clustering.
► Faceword definition for video content.
Journal: Pattern Recognition - Volume 45, Issue 7, July 2012, Pages 2489–2498